Hardware-Accelerated VSLAM for Humanoid Robots
Welcome to Chapter 5 of Module 3: The AI-Robot Brain! In this chapter, we'll explore Hardware-Accelerated Visual-Inertial SLAM (VSLAM) for humanoid robots using NVIDIA's advanced tools. Visual-Inertial SLAM (Simultaneous Localization and Mapping) is crucial for humanoid robots to understand their environment, navigate safely, and perform complex tasks. We'll cover NVIDIA Isaac SLAM, Omniverse Replicator for synthetic data generation, and how to implement robust SLAM systems optimized for humanoid applications.
Learning Objectives
By the end of this chapter, you will be able to:
- Understand the principles of Visual-Inertial SLAM and its importance for humanoid robotics
- Implement NVIDIA Isaac SLAM for real-time localization and mapping
- Use Omniverse Replicator to generate synthetic training data for SLAM systems
- Optimize SLAM algorithms for real-time performance on NVIDIA hardware
- Integrate SLAM with humanoid robot perception and navigation systems
- Handle SLAM failures and maintain robust operation in challenging environments
- Evaluate and benchmark SLAM system performance for humanoid applications
- Implement multi-sensor fusion for enhanced SLAM accuracy
Introduction to Visual-Inertial SLAM
Understanding SLAM Fundamentals
Simultaneous Localization and Mapping (SLAM) is a critical capability for autonomous robots, allowing them to build a map of their environment while simultaneously determining their position within that map. For humanoid robots, SLAM is essential for navigation, obstacle avoidance, and interaction with their surroundings.
Visual-Inertial SLAM combines data from cameras and Inertial Measurement Units (IMUs) to achieve robust and accurate localization and mapping. The visual component provides rich environmental information and feature tracking, while the IMU provides high-frequency motion data that helps maintain tracking during fast movements or when visual features are scarce.
Why Visual-Inertial SLAM for Humanoid Robots?
Humanoid robots present unique challenges and requirements for SLAM systems:
- Dynamic Motion: Humanoid robots experience complex 6-DOF motion patterns that require robust motion estimation
- Height Variation: Changing heights and perspectives as the robot moves affect visual perception
- Occlusions: Robot's own body parts can occlude camera views
- Real-time Requirements: Humanoid robots need immediate responses for balance and navigation
- Multi-sensor Integration: Integration with other sensors like LiDAR, depth cameras, and joint encoders
- Social Navigation: Understanding and navigating around humans safely
SLAM Architecture Components
import numpy as np
from typing import Dict, List, Tuple, Optional
import cv2
from dataclasses import dataclass
@dataclass
class SLAMState:
"""Represents the state of the SLAM system"""
position: np.ndarray # 3D position [x, y, z]
orientation: np.ndarray # Quaternion [w, x, y, z]
velocity: np.ndarray # 3D velocity [vx, vy, vz]
covariance: np.ndarray # State covariance matrix
timestamp: float # Timestamp of state
@dataclass
class FeaturePoint:
"""Represents a visual feature point"""
id: int
position_3d: np.ndarray # 3D world coordinates
position_2d: np.ndarray # 2D image coordinates
descriptor: np.ndarray # Feature descriptor
track_length: int # Number of frames tracked
reprojection_error: float # Error in reprojection
class VisualInertialSLAM:
"""Core Visual-Inertial SLAM implementation"""
def __init__(self):
# Core SLAM components
self.state = SLAMState(
position=np.zeros(3),
orientation=np.array([1, 0, 0, 0]), # Identity quaternion
velocity=np.zeros(3),
covariance=np.eye(6) * 0.1,
timestamp=0.0
)
self.map_points: Dict[int, FeaturePoint] = {}
self.keyframes = []
self.imu_buffer = []
# Feature tracking
self.feature_detector = cv2.ORB_create(nfeatures=2000)
self.descriptor_matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=False)
# IMU integration
self.imu_integration = IMUIntegrator()
# Optimization backend
self.backend_optimizer = PoseGraphOptimizer()
def process_frame(
self,
image: np.ndarray,
imu_data: Dict,
timestamp: float
) -> SLAMState:
"""Process a new frame with image and IMU data"""
# Step 1: Extract and track features
features = self.extract_features(image)
# Step 2: Integrate IMU data for motion prediction
predicted_motion = self.imu_integration.integrate(imu_data, timestamp)
# Step 3: Estimate pose using visual-inertial fusion
estimated_pose = self.estimate_pose(features, predicted_motion)
# Step 4: Update SLAM state
self.state = self.update_state(estimated_pose, timestamp)
# Step 5: Add keyframe if significant motion detected
if self.should_add_keyframe():
self.add_keyframe(image, self.state)
# Step 6: Optimize pose graph
self.backend_optimizer.optimize()
return self.state
def extract_features(self, image: np.ndarray) -> List[FeaturePoint]:
"""Extract visual features from image"""
# Convert to grayscale if needed
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = image
# Detect keypoints and compute descriptors
keypoints = self.feature_detector.detectAndCompute(gray, None)
if keypoints is None:
return []
kp_list, descriptors = keypoints
# Convert to FeaturePoint objects
features = []
for i, (kp, desc) in enumerate(zip(kp_list, descriptors)):
feature = FeaturePoint(
id=i,
position_3d=np.array([0, 0, 0]), # Will be triangulated later
position_2d=np.array([kp.pt[0], kp.pt[1]]),
descriptor=desc,
track_length=1,
reprojection_error=0.0
)
features.append(feature)
return features
def estimate_pose(
self,
features: List[FeaturePoint],
imu_prediction: Dict
) -> Dict:
"""Estimate camera pose using visual-inertial fusion"""
# Visual pose estimation (PnP or similar)
visual_pose = self.estimate_visual_pose(features)
# Fuse with IMU prediction
fused_pose = self.fuse_visual_inertial(
visual_pose,
imu_prediction
)
return fused_pose
def estimate_visual_pose(self, features: List[FeaturePoint]) -> Dict:
"""Estimate pose using visual features"""
# This would implement PnP, direct methods, or other visual pose estimation
return {'R': np.eye(3), 't': np.zeros(3), 'success': True}
def fuse_visual_inertial(
self,
visual_pose: Dict,
imu_prediction: Dict
) -> Dict:
"""Fuse visual and inertial pose estimates"""
# Extended Kalman Filter or optimization-based fusion
# This is a simplified version
fused_rotation = self.interpolate_rotations(
visual_pose['R'],
imu_prediction['R'],
weight=0.7 # Higher weight for visual when features are good
)
fused_translation = 0.8 * visual_pose['t'] + 0.2 * imu_prediction['t']
return {
'R': fused_rotation,
't': fused_translation,
'success': visual_pose['success']
}
def interpolate_rotations(self, R1: np.ndarray, R2: np.ndarray, weight: float) -> np.ndarray:
"""Interpolate between two rotation matrices"""
# Convert to quaternions for proper interpolation
q1 = self.rotation_matrix_to_quaternion(R1)
q2 = self.rotation_matrix_to_quaternion(R2)
# Spherical linear interpolation (SLERP)
q_fused = self.slerp(q1, q2, weight)
return self.quaternion_to_rotation_matrix(q_fused)
def rotation_matrix_to_quaternion(self, R: np.ndarray) -> np.ndarray:
"""Convert rotation matrix to quaternion"""
# Implementation of rotation matrix to quaternion conversion
trace = np.trace(R)
if trace > 0:
s = np.sqrt(trace + 1.0) * 2 # s = 4 * qw
qw = 0.25 * s
qx = (R[2, 1] - R[1, 2]) / s
qy = (R[0, 2] - R[2, 0]) / s
qz = (R[1, 0] - R[0, 1]) / s
else:
if R[0, 0] > R[1, 1] and R[0, 0] > R[2, 2]:
s = np.sqrt(1.0 + R[0, 0] - R[1, 1] - R[2, 2]) * 2
qw = (R[2, 1] - R[1, 2]) / s
qx = 0.25 * s
qy = (R[0, 1] + R[1, 0]) / s
qz = (R[0, 2] + R[2, 0]) / s
elif R[1, 1] > R[2, 2]:
s = np.sqrt(1.0 + R[1, 1] - R[0, 0] - R[2, 2]) * 2
qw = (R[0, 2] - R[2, 0]) / s
qx = (R[0, 1] + R[1, 0]) / s
qy = 0.25 * s
qz = (R[1, 2] + R[2, 1]) / s
else:
s = np.sqrt(1.0 + R[2, 2] - R[0, 0] - R[1, 1]) * 2
qw = (R[1, 0] - R[0, 1]) / s
qx = (R[0, 2] + R[2, 0]) / s
qy = (R[1, 2] + R[2, 1]) / s
qz = 0.25 * s
return np.array([qw, qx, qy, qz])
def quaternion_to_rotation_matrix(self, q: np.ndarray) -> np.ndarray:
"""Convert quaternion to rotation matrix"""
qw, qx, qy, qz = q
R = np.array([
[1 - 2*(qy**2 + qz**2), 2*(qx*qy - qw*qz), 2*(qx*qz + qw*qy)],
[2*(qx*qy + qw*qz), 1 - 2*(qx**2 + qz**2), 2*(qy*qz - qw*qx)],
[2*(qx*qz - qw*qy), 2*(qy*qz + qw*qx), 1 - 2*(qx**2 + qy**2)]
])
return R
def slerp(self, q1: np.ndarray, q2: np.ndarray, t: float) -> np.ndarray:
"""Spherical linear interpolation between quaternions"""
# Normalize quaternions
q1 = q1 / np.linalg.norm(q1)
q2 = q2 / np.linalg.norm(q2)
# Calculate dot product
dot = np.dot(q1, q2)
# If dot product is negative, use negated quaternion
if dot < 0.0:
q2 = -q2
dot = -dot
# Ensure dot product is within valid range
dot = np.clip(dot, -1.0, 1.0)
# Calculate angle
theta_0 = np.arccos(dot)
sin_theta_0 = np.sin(theta_0)
if sin_theta_0 > 0.001: # Avoid division by zero
theta = theta_0 * t
sin_theta = np.sin(theta)
s1 = np.cos(theta) - dot * sin_theta / sin_theta_0
s2 = sin_theta / sin_theta_0
else:
# Linear interpolation for very small angles
s1 = 1.0 - t
s2 = t
return s1 * q1 + s2 * q2
def update_state(self, pose_estimate: Dict, timestamp: float) -> SLAMState:
"""Update SLAM state with new pose estimate"""
# Convert pose estimate to state representation
R = pose_estimate['R']
t = pose_estimate['t']
# Convert rotation matrix to quaternion
orientation = self.rotation_matrix_to_quaternion(R)
# Update state
self.state.position = t
self.state.orientation = orientation
self.state.timestamp = timestamp
return self.state
def should_add_keyframe(self) -> bool:
"""Determine if a new keyframe should be added"""
# Criteria for keyframe selection:
# - Significant motion (rotation/translation threshold)
# - Sufficient time has passed
# - Good feature distribution
return True # Simplified for example
def add_keyframe(self, image: np.ndarray, state: SLAMState):
"""Add a new keyframe to the map"""
keyframe = {
'image': image,
'state': state,
'features': self.extract_features(image)
}
self.keyframes.append(keyframe)
class IMUIntegrator:
"""Handles IMU data integration for motion prediction"""
def __init__(self):
self.gravity = np.array([0, 0, -9.81])
self.bias_acc = np.zeros(3)
self.bias_gyro = np.zeros(3)
self.last_timestamp = 0.0
self.last_velocity = np.zeros(3)
self.last_position = np.zeros(3)
def integrate(self, imu_data: Dict, timestamp: float) -> Dict:
"""Integrate IMU data to predict motion"""
if self.last_timestamp == 0.0:
self.last_timestamp = timestamp
return {'R': np.eye(3), 't': np.zeros(3)}
dt = timestamp - self.last_timestamp
# Remove biases
acc_unbiased = imu_data['acceleration'] - self.bias_acc
gyro_unbiased = imu_data['angular_velocity'] - self.bias_gyro
# Integrate angular velocity to get rotation
delta_angle = gyro_unbiased * dt
R_delta = self.rodrigues_rotation(delta_angle)
# Transform acceleration to global frame and integrate
acc_global = R_delta @ acc_unbiased + self.gravity
delta_velocity = acc_global * dt
delta_position = self.last_velocity * dt + 0.5 * acc_global * dt**2
# Update states
self.last_velocity += delta_velocity
self.last_position += delta_position
self.last_timestamp = timestamp
return {
'R': R_delta,
't': delta_position,
'velocity': self.last_velocity
}
def rodrigues_rotation(self, angle_axis: np.ndarray) -> np.ndarray:
"""Convert angle-axis representation to rotation matrix"""
angle = np.linalg.norm(angle_axis)
if angle < 1e-6:
return np.eye(3)
axis = angle_axis / angle
cos_a = np.cos(angle)
sin_a = np.sin(angle)
cross_matrix = np.array([
[0, -axis[2], axis[1]],
[axis[2], 0, -axis[0]],
[-axis[1], axis[0], 0]
])
R = np.eye(3) + sin_a * cross_matrix + (1 - cos_a) * (cross_matrix @ cross_matrix)
return R
class PoseGraphOptimizer:
"""Optimizes pose graph for globally consistent map"""
def __init__(self):
self.poses = []
self.constraints = []
def add_constraint(self, from_id: int, to_id: int, relative_pose: Dict):
"""Add a relative pose constraint between keyframes"""
constraint = {
'from': from_id,
'to': to_id,
'relative_pose': relative_pose
}
self.constraints.append(constraint)
def optimize(self):
"""Optimize the pose graph using bundle adjustment or graph optimization"""
# This would implement pose graph optimization
# Could use g2o, Ceres Solver, or custom optimization
pass
NVIDIA Isaac SLAM Implementation
Overview of NVIDIA Isaac SLAM
NVIDIA Isaac SLAM is a comprehensive solution that leverages NVIDIA's GPU acceleration for real-time visual-inertial SLAM. It includes optimized algorithms for feature detection, tracking, pose estimation, and mapping that take advantage of CUDA cores and Tensor cores for maximum performance.
Setting Up Isaac SLAM
import carb
import omni
from omni.isaac.core import World
from omni.isaac.core.utils.stage import add_reference_to_stage
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.sensor import Camera, Imu
from omni.isaac.core.materials import PhysicsMaterial
from omni.isaac.core.objects import DynamicCuboid
from omni.isaac.core.utils.viewports import set_camera_view
from pxr import Gf, UsdGeom, Sdf
class IsaacSLAMSystem:
"""Integration of SLAM with Isaac Sim environment"""
def __init__(self, world: World):
self.world = world
self.slam_pipeline = None
self.cameras = []
self.imus = []
self.slam_initialized = False
def setup_sensors(self, robot_prim_path: str):
"""Setup cameras and IMUs for SLAM"""
# Add stereo cameras for visual SLAM
left_camera = self.add_camera(
prim_path=f"{robot_prim_path}/left_camera",
position=[0.1, -0.05, 0.1],
orientation=[1, 0, 0, 0]
)
right_camera = self.add_camera(
prim_path=f"{robot_prim_path}/right_camera",
position=[0.1, 0.05, 0.1],
orientation=[1, 0, 0, 0]
)
self.cameras = [left_camera, right_camera]
# Add IMU sensor
imu_sensor = self.add_imu(
prim_path=f"{robot_prim_path}/imu",
position=[0, 0, 0.15],
orientation=[1, 0, 0, 0]
)
self.imus = [imu_sensor]
def add_camera(self, prim_path: str, position: list, orientation: list):
"""Add a camera sensor to the robot"""
camera = Camera(
prim_path=prim_path,
frequency=30, # 30 Hz
resolution=(640, 480),
position=position,
orientation=orientation
)
# Enable depth and segmentation data
camera.add_data_type("depth")
camera.add_data_type("rgb")
camera.add_data_type("instance_segmentation")
return camera
def add_imu(self, prim_path: str, position: list, orientation: list):
"""Add an IMU sensor to the robot"""
imu = Imu(
prim_path=prim_path,
frequency=200, # 200 Hz for high-frequency motion data
position=position,
orientation=orientation
)
return imu
def initialize_slam_pipeline(self):
"""Initialize the SLAM pipeline using Isaac tools"""
# Import Isaac SLAM modules
try:
from omni.isaac.orbit_assets import SLAMPipeline
from omni.isaac.core.utils import nucleus
except ImportError:
print("Isaac SLAM extensions not available, using custom implementation")
self.slam_pipeline = VisualInertialSLAM()
self.slam_initialized = True
return
# Initialize SLAM pipeline
self.slam_pipeline = SLAMPipeline(
camera_resolution=(640, 480),
imu_frequency=200,
max_features=2000,
feature_quality=0.01,
backend="cuda"
)
# Configure SLAM parameters
self.slam_pipeline.set_parameters({
"tracking_threshold": 10,
"relocalization_threshold": 50,
"loop_closure_enabled": True,
"bundle_adjustment_frequency": 10,
"map_merging_enabled": True
})
self.slam_initialized = True
def process_sensor_data(self):
"""Process sensor data through SLAM pipeline"""
if not self.slam_initialized:
return None
# Get camera data
camera_data = {}
for i, camera in enumerate(self.cameras):
rgb_data = camera.get_rgb_data()
depth_data = camera.get_depth_data()
camera_data[f'camera_{i}'] = {
'rgb': rgb_data,
'depth': depth_data,
'timestamp': camera.last_frame_time
}
# Get IMU data
imu_data = {}
for i, imu in enumerate(self.imus):
linear_accel = imu.get_linear_acceleration()
angular_vel = imu.get_angular_velocity()
imu_data[f'imu_{i}'] = {
'acceleration': linear_accel,
'angular_velocity': angular_vel,
'timestamp': imu.last_frame_time
}
# Process through SLAM pipeline
slam_result = self.slam_pipeline.process(
camera_data=camera_data,
imu_data=imu_data
)
return slam_result
def get_current_map(self):
"""Get the current map from SLAM system"""
if not self.slam_initialized or self.slam_pipeline is None:
return None
return self.slam_pipeline.get_map()
def get_robot_pose(self):
"""Get current robot pose from SLAM"""
if not self.slam_initialized or self.slam_pipeline is None:
return None
return self.slam_pipeline.get_current_pose()
def reset_slam(self):
"""Reset SLAM system"""
if self.slam_pipeline:
self.slam_pipeline.reset()
# Clear all stored data
self.cameras = []
self.imus = []
self.slam_initialized = False
class IsaacSLAMOrchestrator:
"""Orchestrates SLAM operations within Isaac Sim"""
def __init__(self, world: World):
self.world = world
self.slam_system = IsaacSLAMSystem(world)
self.navigation_planner = None
self.localization_accuracy = 0.0
def setup_robot_slam(self, robot_name: str = "/World/Robot"):
"""Setup SLAM for a specific robot"""
# Setup sensors
self.slam_system.setup_sensors(robot_name)
# Initialize SLAM pipeline
self.slam_system.initialize_slam_pipeline()
# Setup navigation planner
self.navigation_planner = PathPlanner()
print(f"SLAM system initialized for robot: {robot_name}")
def run_slam_loop(self, max_iterations: int = 1000):
"""Run the main SLAM processing loop"""
for iteration in range(max_iterations):
# Step the world simulation
self.world.step(render=True)
# Process sensor data through SLAM
slam_result = self.slam_system.process_sensor_data()
if slam_result:
# Update localization accuracy
self.localization_accuracy = slam_result.get('accuracy', 0.0)
# Update navigation planner with new map
current_map = self.slam_system.get_current_map()
if current_map:
self.navigation_planner.update_map(current_map)
# Log SLAM status
self.log_slam_status(slam_result, iteration)
# Check for termination conditions
if self.should_terminate():
break
def log_slam_status(self, slam_result: Dict, iteration: int):
"""Log SLAM system status"""
pose = slam_result.get('pose', {})
tracking_status = slam_result.get('tracking_status', 'UNKNOWN')
print(f"Iteration {iteration}: "
f"Position: {pose.get('position', [0,0,0])}, "
f"Tracking: {tracking_status}, "
f"Accuracy: {self.localization_accuracy:.3f}m")
def should_terminate(self) -> bool:
"""Check if SLAM loop should terminate"""
# Implement termination logic based on accuracy, tracking status, etc.
return False
def get_localization_metrics(self) -> Dict:
"""Get current localization metrics"""
return {
'position_accuracy': self.localization_accuracy,
'tracking_quality': self.get_tracking_quality(),
'map_coverage': self.get_map_coverage(),
'feature_density': self.get_feature_density()
}
def get_tracking_quality(self) -> float:
"""Get current tracking quality metric"""
# This would interface with the SLAM system to get tracking quality
return 0.9 # Placeholder
def get_map_coverage(self) -> float:
"""Get map coverage metric"""
# This would calculate map coverage based on explored areas
return 0.7 # Placeholder
def get_feature_density(self) -> float:
"""Get feature density in current view"""
# This would calculate feature density from current camera view
return 50.0 # Features per square meter, placeholder
Omniverse Replicator for Synthetic SLAM Data
Introduction to Synthetic Data Generation
Omniverse Replicator is NVIDIA's synthetic data generation tool that creates photorealistic training data for AI models. For SLAM systems, synthetic data is crucial for training robust perception algorithms that can handle diverse environments and conditions.
Generating SLAM Training Data
from omni.replicator.core import Replicator, random_colours, on_time, sphere_points
from omni.replicator.core.utils import get_ext_template
import omni.kit.commands
from pxr import Gf, UsdGeom, Sdf, UsdShade
import numpy as np
import json
from typing import Dict, List, Tuple
class SLAMDataGenerator:
"""Generates synthetic data for training SLAM systems"""
def __init__(self):
self.replicator = Replicator()
self.scene_config = {}
self.annotation_schemas = []
self.data_output_dir = "/path/to/slam/dataset"
def setup_replicator(self):
"""Setup Omniverse Replicator for SLAM data generation"""
# Create annotators for SLAM-relevant data
self.replicator.add_annotator("bbox", init_params={"semantic": True})
self.replicator.add_annotator("instance", init_params={"semantic": True})
self.replicator.add_annotator("depth", init_params={"type": "distance"})
self.replicator.add_annotator("normal", init_params={})
self.replicator.add_annotator("motion_vector", init_params={})
# Add camera intrinsic annotators
self.replicator.add_annotator("camera", init_params={})
def define_scene_variations(self):
"""Define scene variations for synthetic SLAM data"""
# Lighting variations
lighting_config = {
"light_types": ["dome", "directional", "sphere"],
"intensity_range": [0.5, 2.0],
"color_temperatures": [3000, 6500, 10000], # Kelvin
"shadow_softness": [0.1, 0.8]
}
# Environment variations
environment_config = {
"indoor_scenes": [
"office", "warehouse", "home", "laboratory", "corridor"
],
"outdoor_scenes": [
"park", "street", "plaza", "garden", "construction_site"
],
"object_densities": [0.1, 0.8], # Objects per square meter
"texture_variations": ["smooth", "rough", "reflective", "matte"]
}
# Camera variations
camera_config = {
"focal_lengths": [18, 24, 35, 50, 85], # mm
"sensor_sizes": [(36, 24), (23.6, 15.6), (17.3, 13)],
"distortion_coefficients": {
"k1": [-0.5, 0.5],
"k2": [-0.1, 0.1],
"p1": [-0.01, 0.01],
"p2": [-0.01, 0.01]
}
}
# Motion variations
motion_config = {
"linear_velocities": [0.1, 2.0], # m/s
"angular_velocities": [0.1, 2.0], # rad/s
"acceleration_profiles": ["constant", "sinusoidal", "random_walk"]
}
self.scene_config = {
"lighting": lighting_config,
"environment": environment_config,
"camera": camera_config,
"motion": motion_config
}
def generate_training_scenes(self, num_scenes: int = 1000):
"""Generate diverse training scenes for SLAM"""
for scene_idx in range(num_scenes):
print(f"Generating scene {scene_idx + 1}/{num_scenes}")
# Create a new scene
stage = self.create_scene_with_variations()
# Annotate the scene
annotations = self.annotate_scene(stage)
# Save the scene and annotations
self.save_scene_data(stage, annotations, scene_idx)
# Clean up for next scene
stage.Close()
def create_scene_with_variations(self):
"""Create a scene with random variations"""
from pxr import UsdStage, SdfLayer, Gf
# Create new stage
stage = UsdStage.CreateNew(f"slam_scene_{np.random.randint(10000)}.usd")
# Add default prim
world_prim = stage.DefinePrim("/World", "Xform")
# Add ground plane
ground = stage.DefinePrim("/World/Ground", "Mesh")
# Configure ground properties
# Add lighting with random variations
self.add_random_lighting(stage)
# Add objects with random placements
self.add_random_objects(stage)
# Add camera with random parameters
self.add_random_camera(stage)
return stage
def add_random_lighting(self, stage):
"""Add random lighting to the scene"""
import random
light_type = random.choice(self.scene_config["lighting"]["light_types"])
if light_type == "dome":
# Add dome light
dome_light = stage.DefinePrim("/World/DomeLight", "DomeLight")
intensity = random.uniform(*self.scene_config["lighting"]["intensity_range"])
color_temp = random.choice(self.scene_config["lighting"]["color_temperatures"])
# Set dome light properties
dome_light.CreateAttribute("intensity", Sdf.ValueTypeNames.Float).Set(intensity)
elif light_type == "directional":
# Add directional light
dir_light = stage.DefinePrim("/World/DirectionalLight", "DistantLight")
# Set directional light properties
elif light_type == "sphere":
# Add sphere light
sphere_light = stage.DefinePrim("/World/SphereLight", "SphereLight")
# Set sphere light properties
def add_random_objects(self, stage):
"""Add random objects to create SLAM features"""
import random
# Define object types that are good for SLAM (distinctive features)
slam_objects = [
"cubes", "cylinders", "spheres", "pyramids",
"textured_planes", "furniture", "obstacles"
]
num_objects = random.randint(5, 20)
for i in range(num_objects):
obj_type = random.choice(slam_objects[:6]) # Use basic shapes
obj_prim = stage.DefinePrim(f"/World/Object_{i}", "Xform")
# Random position
x = random.uniform(-5, 5)
y = random.uniform(-5, 5)
z = random.uniform(0, 2)
obj_prim.CreateAttribute("xformOp:translate", Sdf.ValueTypeNames.Float3).Set(Gf.Vec3f(x, y, z))
# Add distinctive textures/properties for feature detection
self.add_distinctive_features(obj_prim)
def add_distinctive_features(self, prim):
"""Add features that are good for visual SLAM"""
# This would add textures, patterns, or properties that create good visual features
pass
def add_random_camera(self, stage):
"""Add camera with random parameters for SLAM"""
from omni.isaac.core.utils.prims import define_prim
# Define camera prim
camera_prim = stage.DefinePrim("/World/Camera", "Camera")
# Set random camera parameters based on config
focal_length = random.choice(self.scene_config["camera"]["focal_lengths"])
sensor_size = random.choice(self.scene_config["camera"]["sensor_sizes"])
camera_prim.CreateAttribute("focalLength", Sdf.ValueTypeNames.Float).Set(focal_length)
camera_prim.CreateAttribute("horizontalAperture", Sdf.ValueTypeNames.Float).Set(sensor_size[0])
camera_prim.CreateAttribute("verticalAperture", Sdf.ValueTypeNames.Float).Set(sensor_size[1])
def annotate_scene(self, stage):
"""Generate annotations for the scene"""
# This would use Replicator annotators to generate ground truth data
annotations = {
"camera_poses": self.generate_camera_poses(),
"feature_points": self.generate_feature_points(),
"depth_maps": self.generate_depth_data(),
"optical_flow": self.generate_optical_flow(),
"imu_data": self.generate_imu_simulation()
}
return annotations
def generate_camera_poses(self) -> List[Dict]:
"""Generate camera poses for SLAM trajectory"""
poses = []
# Generate a realistic trajectory (e.g., random walk with smooth turns)
for frame in range(100): # 100 frames of trajectory
# Random motion with smooth transitions
position = np.array([
frame * 0.1 + np.random.normal(0, 0.05),
np.sin(frame * 0.1) * 0.5 + np.random.normal(0, 0.05),
1.0 + np.random.normal(0, 0.02) # Fixed height with small variation
])
# Random orientation (with smooth changes)
orientation = self.generate_smooth_orientation(frame)
poses.append({
"frame": frame,
"position": position.tolist(),
"orientation": orientation.tolist(),
"timestamp": frame * 0.033 # Assuming 30 FPS
})
return poses
def generate_smooth_orientation(self, frame: int) -> np.ndarray:
"""Generate smooth orientation changes for realistic motion"""
# Use quaternion slerp for smooth rotation
base_quat = np.array([1, 0, 0, 0]) # Identity
# Add small random rotations for natural movement
angle = np.random.normal(0, 0.1)
axis = np.random.normal(0, 1, 3)
axis = axis / np.linalg.norm(axis) # Normalize
# Convert axis-angle to quaternion
s = np.sin(angle / 2)
orientation = np.array([
np.cos(angle / 2),
s * axis[0],
s * axis[1],
s * axis[2]
])
return orientation
def generate_feature_points(self) -> List[Dict]:
"""Generate 3D feature points for SLAM"""
features = []
# Generate feature points in the scene
for i in range(500): # 500 feature points
# Random 3D position in scene bounds
pos = np.array([
np.random.uniform(-5, 5),
np.random.uniform(-5, 5),
np.random.uniform(0, 3)
])
# Feature descriptor (simplified)
descriptor = np.random.random(128).astype(np.float32)
features.append({
"id": i,
"position": pos.tolist(),
"descriptor": descriptor.tolist(),
"visibility": [] # Will be filled with frame visibility
})
return features
def generate_depth_data(self) -> Dict:
"""Generate synthetic depth data"""
return {
"format": "distance",
"resolution": [640, 480],
"data": [] # Would contain depth values
}
def generate_optical_flow(self) -> Dict:
"""Generate synthetic optical flow data"""
return {
"format": "2d_vectors",
"resolution": [640, 480],
"data": [] # Would contain flow vectors
}
def generate_imu_simulation(self) -> List[Dict]:
"""Generate simulated IMU data for SLAM"""
imu_data = []
for frame in range(100):
# Generate realistic IMU readings
# Accelerometer (with gravity and motion)
gravity = np.array([0, 0, -9.81])
motion_acc = np.random.normal(0, 0.1, 3) # Small motion accelerations
acc = gravity + motion_acc + np.random.normal(0, 0.01, 3) # Add noise
# Gyroscope (angular velocities)
ang_vel = np.random.normal(0, 0.1, 3) # Small angular velocities with noise
imu_data.append({
"frame": frame,
"acceleration": acc.tolist(),
"angular_velocity": ang_vel.tolist(),
"timestamp": frame * 0.005 # 200 Hz IMU
})
return imu_data
def save_scene_data(self, stage, annotations: Dict, scene_idx: int):
"""Save scene data and annotations"""
import os
# Create output directory if it doesn't exist
os.makedirs(self.data_output_dir, exist_ok=True)
# Save USD stage
stage_path = os.path.join(self.data_output_dir, f"scene_{scene_idx:04d}.usd")
stage.Export(stage_path)
# Save annotations
annotation_path = os.path.join(self.data_output_dir, f"scene_{scene_idx:04d}_annotations.json")
with open(annotation_path, 'w') as f:
json.dump(annotations, f, indent=2)
class SLAMDatasetBuilder:
"""Builds complete SLAM training datasets using synthetic data"""
def __init__(self):
self.generator = SLAMDataGenerator()
self.dataset_stats = {}
self.validation_metrics = {}
def build_dataset(self, config: Dict):
"""Build a complete SLAM dataset"""
print("Setting up Replicator...")
self.generator.setup_replicator()
print("Defining scene variations...")
self.generator.define_scene_variations()
print(f"Generating {config['num_scenes']} training scenes...")
self.generator.generate_training_scenes(config['num_scenes'])
print("Calculating dataset statistics...")
self.dataset_stats = self.calculate_dataset_statistics()
print("Validating dataset quality...")
self.validation_metrics = self.validate_dataset_quality()
print("Dataset building complete!")
return self.get_build_report()
def calculate_dataset_statistics(self) -> Dict:
"""Calculate statistics about the generated dataset"""
return {
"total_scenes": 1000,
"scene_types": {
"indoor": 600,
"outdoor": 400
},
"feature_density": 45.2, # Features per frame
"trajectory_complexity": 0.7, # Scale 0-1
"lighting_variations": 12, # Number of different lighting conditions
"object_variations": 8 # Number of different object types
}
def validate_dataset_quality(self) -> Dict:
"""Validate the quality of generated dataset"""
return {
"feature_visibility": 0.85, # Percentage of features visible
"motion_diversity": 0.92, # How diverse the motions are
"annotation_accuracy": 0.99, # Ground truth accuracy
"realism_score": 0.88, # How realistic the data looks
"training_readiness": 0.91 # Overall readiness for training
}
def get_build_report(self) -> Dict:
"""Get complete build report"""
return {
"dataset_stats": self.dataset_stats,
"validation_metrics": self.validation_metrics,
"output_path": self.generator.data_output_dir,
"generation_time": "2.5 hours", # Placeholder
"file_size": "15 GB" # Placeholder
}
def generate_evaluation_scenes(self, num_scenes: int = 100):
"""Generate separate evaluation scenes"""
print(f"Generating {num_scenes} evaluation scenes...")
# Use different parameters for evaluation to avoid overfitting
eval_config = self.generator.scene_config.copy()
# Modify config for evaluation scenes
# Generate evaluation scenes
for scene_idx in range(num_scenes):
# Create evaluation scene
stage = self.generator.create_scene_with_variations()
# Special evaluation annotations
eval_annotations = self.generate_evaluation_annotations(stage)
# Save evaluation data
self.generator.save_scene_data(
stage,
eval_annotations,
f"eval_{scene_idx:04d}"
)
stage.Close()
def generate_evaluation_annotations(self, stage) -> Dict:
"""Generate evaluation-specific annotations"""
# This would generate more detailed ground truth for evaluation
return {
"ground_truth_poses": self.generate_ground_truth_poses(),
"metric_annotations": self.generate_metric_annotations(),
"benchmark_targets": self.generate_benchmark_targets()
}
def generate_ground_truth_poses(self) -> List[Dict]:
"""Generate precise ground truth poses for evaluation"""
# More precise trajectory generation for evaluation
return []
def generate_metric_annotations(self) -> Dict:
"""Generate annotations for SLAM metrics calculation"""
return {}
def generate_benchmark_targets(self) -> List[str]:
"""Generate list of benchmark targets for evaluation"""
return [
"absolute_trajectory_error",
"relative_pose_error",
"map_accuracy",
"tracking_stability"
]
Hardware Acceleration with NVIDIA GPUs
GPU-Accelerated Feature Detection
Modern SLAM systems leverage GPU acceleration to achieve real-time performance. NVIDIA GPUs provide specialized hardware for computer vision tasks including feature detection, matching, and optimization.
import cupy as cp
import numpy as np
from numba import cuda
import pycuda.driver as cuda_driver
import pycuda.autoinit
from pycuda.compiler import SourceModule
class GPUFeatureDetector:
"""GPU-accelerated feature detection for SLAM"""
def __init__(self, max_features: int = 2000):
self.max_features = max_features
# CUDA kernels for feature detection
self.corner_kernel = self.compile_corner_kernel()
self.descriptor_kernel = self.compile_descriptor_kernel()
# GPU memory management
self.gpu_image_buffer = None
self.gpu_features_buffer = None
def compile_corner_kernel(self):
"""Compile CUDA kernel for corner detection"""
corner_cuda_code = """
__global__ void harris_corner_kernel(
float* image,
float* corners,
int width,
int height,
float threshold
) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < 1 || x >= width - 1 || y < 1 || y >= height - 1) return;
int idx = y * width + x;
// Compute image gradients
float dx = image[y * width + (x + 1)] - image[y * width + (x - 1)];
float dy = image[(y + 1) * width + x] - image[(y - 1) * width + x];
// Compute second moment matrix elements
float Ixx = dx * dx;
float Iyy = dy * dy;
float Ixy = dx * dy;
// Harris corner response
float det = Ixx * Iyy - Ixy * Ixy;
float trace = Ixx + Iyy;
float response = det - 0.04f * trace * trace;
if (response > threshold) {
corners[idx] = response;
} else {
corners[idx] = 0.0f;
}
}
"""
mod = SourceModule(corner_cuda_code)
return mod.get_function("harris_corner_kernel")
def compile_descriptor_kernel(self):
"""Compile CUDA kernel for descriptor computation"""
desc_cuda_code = """
__global__ void brief_descriptor_kernel(
float* image,
int* keypoints,
float* descriptors,
int num_keypoints,
int width,
int height
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= num_keypoints) return;
int x = keypoints[idx * 2];
int y = keypoints[idx * 2 + 1];
// Compute BRIEF descriptor (simplified)
float desc[32]; // 256-bit descriptor
for (int i = 0; i < 32; i++) {
desc[i] = 0.0f;
}
// Sample pairs of pixels and compare intensities
for (int i = 0; i < 256; i++) {
int dx1 = (i * 7) % 15 - 7; // Random offset
int dy1 = (i * 11) % 15 - 7;
int dx2 = (i * 13) % 15 - 7;
int dy2 = (i * 17) % 15 - 7;
float val1 = image[(y + dy1) * width + (x + dx1)];
float val2 = image[(y + dy2) * width + (x + dx2)];
if (val1 > val2) {
int desc_idx = i / 32;
int bit_idx = i % 32;
desc[desc_idx] |= (1 << bit_idx);
}
}
// Store descriptor
for (int i = 0; i < 32; i++) {
descriptors[idx * 32 + i] = desc[i];
}
}
"""
mod = SourceModule(desc_cuda_code)
return mod.get_function("brief_descriptor_kernel")
def detect_features_gpu(self, image: np.ndarray) -> Dict:
"""Detect features using GPU acceleration"""
# Transfer image to GPU
h, w = image.shape[:2]
gpu_image = cp.asarray(image.astype(np.float32))
# Detect corners
corners = cp.zeros_like(gpu_image)
block_size = (16, 16)
grid_size = ((w + block_size[0] - 1) // block_size[0],
(h + block_size[1] - 1) // block_size[1])
self.corner_kernel(
gpu_image,
corners,
np.int32(w),
np.int32(h),
np.float32(0.0001),
block=block_size,
grid=grid_size
)
# Extract corner points
corner_points = cp.argwhere(corners > 0.0001)
corner_responses = corners[corner_points[:, 0], corner_points[:, 1]]
# Sort by response strength and keep top features
if len(corner_points) > self.max_features:
top_indices = cp.argsort(corner_responses)[-self.max_features:]
corner_points = corner_points[top_indices]
return {
'keypoints': corner_points.get(),
'responses': corner_responses.get(),
'count': len(corner_points)
}
def compute_descriptors_gpu(self, image: np.ndarray, keypoints: np.ndarray) -> np.ndarray:
"""Compute descriptors for keypoints using GPU"""
if len(keypoints) == 0:
return np.array([])
# Transfer data to GPU
gpu_image = cp.asarray(image.astype(np.float32))
gpu_keypoints = cp.asarray(keypoints.astype(np.int32))
gpu_descriptors = cp.zeros((len(keypoints), 32), dtype=cp.float32)
# Compute descriptors
block_size = (256,)
grid_size = ((len(keypoints) + block_size[0] - 1) // block_size[0],)
self.descriptor_kernel(
gpu_image,
gpu_keypoints,
gpu_descriptors,
np.int32(len(keypoints)),
np.int32(image.shape[1]),
np.int32(image.shape[0]),
block=block_size,
grid=grid_size
)
return gpu_descriptors.get()
class GPUSLAMBackend:
"""GPU-accelerated backend for SLAM optimization"""
def __init__(self):
self.pose_graph_optimizer = GPUOptimizer()
self.bundle_adjuster = GPUBundleAdjuster()
self.triangulator = GPUTriangulator()
def optimize_pose_graph_gpu(self, poses: np.ndarray, constraints: List[Dict]) -> np.ndarray:
"""Optimize pose graph using GPU acceleration"""
return self.pose_graph_optimizer.optimize(poses, constraints)
def run_bundle_adjustment_gpu(
self,
camera_poses: np.ndarray,
points_3d: np.ndarray,
observations: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
"""Run bundle adjustment using GPU"""
return self.bundle_adjuster.optimize(camera_poses, points_3d, observations)
def triangulate_points_gpu(
self,
points_2d: List[np.ndarray],
camera_poses: List[np.ndarray]
) -> np.ndarray:
"""Triangulate 3D points from 2D observations"""
return self.triangulator.triangulate(points_2d, camera_poses)
class GPUOptimizer:
"""GPU-accelerated optimization for SLAM"""
def __init__(self):
# Initialize CUDA contexts and memory pools
self.max_iterations = 100
self.convergence_threshold = 1e-6
def optimize(self, poses: np.ndarray, constraints: List[Dict]) -> np.ndarray:
"""Optimize poses using GPU"""
# Transfer to GPU
gpu_poses = cp.asarray(poses.astype(cp.float64))
gpu_constraints = self.prepare_constraints_gpu(constraints)
# Run optimization iterations
for iteration in range(self.max_iterations):
prev_poses = gpu_poses.copy()
# Compute residuals and Jacobians on GPU
residuals, jacobians = self.compute_system_gpu(gpu_poses, gpu_constraints)
# Solve normal equations using Cholesky decomposition on GPU
delta = self.solve_normal_equations_gpu(jacobians, residuals)
# Update poses
gpu_poses = gpu_poses - delta
# Check for convergence
if cp.linalg.norm(delta) < self.convergence_threshold:
break
return gpu_poses.get()
def prepare_constraints_gpu(self, constraints: List[Dict]):
"""Prepare constraints for GPU processing"""
# Convert constraints to GPU-friendly format
return cp.array([c['relative_pose'] for c in constraints])
def compute_system_gpu(self, poses, constraints):
"""Compute residual and Jacobian system on GPU"""
# Implementation would compute residuals and Jacobians using CUDA
residuals = cp.zeros(100) # Placeholder
jacobians = cp.zeros((100, 6)) # Placeholder
return residuals, jacobians
def solve_normal_equations_gpu(self, jacobians, residuals):
"""Solve normal equations using GPU"""
# J^T * J * delta = -J^T * r
JTJ = jacobians.T @ jacobians
JTr = -(jacobians.T @ residuals)
# Solve using Cholesky decomposition
L = cp.linalg.cholesky(JTJ)
y = cp.linalg.solve_triangular(L, JTr, lower=True)
delta = cp.linalg.solve_triangular(L.T, y, lower=False)
return delta
class GPUBundleAdjuster:
"""GPU-accelerated bundle adjustment"""
def __init__(self):
self.max_iterations = 50
self.convergence_threshold = 1e-6
def optimize(
self,
camera_poses: np.ndarray,
points_3d: np.ndarray,
observations: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
"""Run bundle adjustment on GPU"""
# Transfer to GPU
gpu_poses = cp.asarray(camera_poses.astype(cp.float64))
gpu_points = cp.asarray(points_3d.astype(cp.float64))
gpu_obs = cp.asarray(observations.astype(cp.float64))
# Optimization loop
for iteration in range(self.max_iterations):
prev_poses = gpu_poses.copy()
prev_points = gpu_points.copy()
# Compute residuals and Jacobians
residuals, pose_jacs, point_jacs = self.compute_ba_system_gpu(
gpu_poses, gpu_points, gpu_obs
)
# Solve using Levenberg-Marquardt on GPU
pose_updates, point_updates = self.solve_ba_gpu(
pose_jacs, point_jacs, residuals
)
# Apply updates
gpu_poses = gpu_poses + pose_updates
gpu_points = gpu_points + point_updates
# Check convergence
pose_norm = cp.linalg.norm(pose_updates)
point_norm = cp.linalg.norm(point_updates)
if pose_norm < self.convergence_threshold and point_norm < self.convergence_threshold:
break
return gpu_poses.get(), gpu_points.get()
def compute_ba_system_gpu(self, poses, points, observations):
"""Compute bundle adjustment system on GPU"""
# This would implement projection and Jacobian computation on GPU
residuals = cp.zeros(1000) # Placeholder
pose_jacs = cp.zeros((1000, 6)) # Placeholder
point_jacs = cp.zeros((1000, 3)) # Placeholder
return residuals, pose_jacs, point_jacs
def solve_ba_gpu(self, pose_jacs, point_jacs, residuals):
"""Solve bundle adjustment system on GPU"""
# Implementation would use Schur complement or other BA-specific methods
pose_updates = cp.zeros(pose_jacs.shape[1])
point_updates = cp.zeros(point_jacs.shape[1])
return pose_updates, point_updates
class GPUTriangulator:
"""GPU-accelerated 3D point triangulation"""
def __init__(self):
self.triangulation_kernel = self.compile_triangulation_kernel()
def compile_triangulation_kernel(self):
"""Compile CUDA kernel for triangulation"""
triangulation_cuda_code = """
__global__ void triangulate_kernel(
float* points_2d_1,
float* points_2d_2,
float* pose_1,
float* pose_2,
float* points_3d,
int num_points
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= num_points) return;
// Extract 2D points
float x1 = points_2d_1[idx * 2];
float y1 = points_2d_1[idx * 2 + 1];
float x2 = points_2d_2[idx * 2];
float y2 = points_2d_2[idx * 2 + 1];
// Extract camera matrices (simplified)
// In practice, this would use full 3x4 projection matrices
float3 C1 = make_float3(pose_1[3], pose_1[7], pose_1[11]); // Camera center 1
float3 C2 = make_float3(pose_2[3], pose_2[7], pose_2[11]); // Camera center 2
// Triangulation using SVD or other method
// Simplified linear triangulation
float3 ray1 = normalize(make_float3(x1, y1, 1.0f));
float3 ray2 = normalize(make_float3(x2, y2, 1.0f));
// Solve for 3D point using linear least squares
// This is a simplified version - full implementation would be more complex
float3 point_3d;
point_3d.x = (C1.x + C2.x) / 2.0f + (ray1.x + ray2.x) * 10.0f;
point_3d.y = (C1.y + C2.y) / 2.0f + (ray1.y + ray2.y) * 10.0f;
point_3d.z = (C1.z + C2.z) / 2.0f + (ray1.z + ray2.z) * 10.0f;
// Store result
points_3d[idx * 3] = point_3d.x;
points_3d[idx * 3 + 1] = point_3d.y;
points_3d[idx * 3 + 2] = point_3d.z;
}
"""
mod = SourceModule(triangulation_cuda_code)
return mod.get_function("triangulate_kernel")
def triangulate(
self,
points_2d: List[np.ndarray],
camera_poses: List[np.ndarray]
) -> np.ndarray:
"""Triangulate 3D points from multiple views using GPU"""
if len(points_2d) < 2:
return np.array([])
# Use first two views for triangulation (in practice, use all available views)
points_1 = points_2d[0]
points_2 = points_2d[1]
pose_1 = camera_poses[0]
pose_2 = camera_poses[1]
if len(points_1) != len(points_2):
# Need to match points between views first
matches = self.match_features(points_1, points_2)
points_1_matched = points_1[matches[:, 0]]
points_2_matched = points_2[matches[:, 1]]
else:
points_1_matched = points_1
points_2_matched = points_2
# Transfer to GPU
gpu_points_1 = cp.asarray(points_1_matched.astype(cp.float32))
gpu_points_2 = cp.asarray(points_2_matched.astype(cp.float32))
gpu_pose_1 = cp.asarray(pose_1.astype(cp.float32))
gpu_pose_2 = cp.asarray(pose_2.astype(cp.float32))
gpu_points_3d = cp.zeros((len(points_1_matched), 3), dtype=cp.float32)
# Run triangulation kernel
block_size = (256,)
grid_size = ((len(points_1_matched) + block_size[0] - 1) // block_size[0],)
self.triangulation_kernel(
gpu_points_1,
gpu_points_2,
gpu_pose_1,
gpu_pose_2,
gpu_points_3d,
np.int32(len(points_1_matched)),
block=block_size,
grid=grid_size
)
return gpu_points_3d.get()
def match_features(self, points_1: np.ndarray, points_2: np.ndarray) -> np.ndarray:
"""Simple feature matching (in practice, use descriptors)"""
# This would implement proper feature matching using descriptors
# For now, return identity mapping
n = min(len(points_1), len(points_2))
return np.column_stack([np.arange(n), np.arange(n)])
Tensor Core Optimization for Deep Learning SLAM
NVIDIA's Tensor Cores provide specialized acceleration for deep learning workloads, which can be leveraged for neural SLAM systems that combine traditional geometric methods with deep learning.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load
import time
class NeuralSLAMModule(nn.Module):
"""Neural SLAM module using Tensor Core acceleration"""
def __init__(self, input_channels: int = 3, feature_dim: int = 256):
super().__init__()
# Feature extraction backbone
self.feature_extractor = nn.Sequential(
# First conv block with Tensor Core-friendly dimensions
nn.Conv2d(input_channels, 64, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
# Tensor Core optimized layers (multiples of 8/16/32/64)
nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 256, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
# Bottleneck for dimensionality reduction
nn.Conv2d(256, feature_dim, kernel_size=1, bias=False),
nn.BatchNorm2d(feature_dim),
nn.ReLU(inplace=True)
)
# Pose estimation head
self.pose_head = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(feature_dim, 512),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(512, 6) # 6-DOF pose (3 translation, 3 rotation)
)
# Depth estimation head
self.depth_head = nn.Sequential(
nn.Conv2d(feature_dim, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 1, kernel_size=1), # Single channel depth map
nn.Sigmoid() # Normalize to [0,1] range
)
# Initialize weights for Tensor Core efficiency
self._initialize_weights()
def _initialize_weights(self):
"""Initialize weights for optimal Tensor Core performance"""
for m in self.modules():
if isinstance(m, nn.Conv2d):
# Use Kaiming initialization for ReLU networks
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
"""Forward pass through neural SLAM module"""
# Extract features
features = self.feature_extractor(x)
# Estimate pose
pose = self.pose_head(features)
# Estimate depth
depth = self.depth_head(features)
# Extract keypoint features (for traditional SLAM integration)
keypoint_features = self.extract_keypoints(features)
return {
'pose': pose,
'depth': depth,
'features': features,
'keypoints': keypoint_features
}
def extract_keypoints(self, feature_map: torch.Tensor) -> torch.Tensor:
"""Extract keypoints from feature map"""
# Apply 3x3 max pooling to find local maxima
pooled = F.max_pool2d(feature_map, kernel_size=3, stride=1, padding=1)
is_max = (feature_map == pooled).float()
# Apply threshold to get sparse keypoints
keypoints = is_max * feature_map
return keypoints
class TensorCoreSLAM:
"""SLAM system optimized for Tensor Core acceleration"""
def __init__(self, device: str = 'cuda'):
self.device = torch.device(device)
# Neural SLAM module
self.neural_slam = NeuralSLAMModule().to(self.device)
# Traditional geometric SLAM for comparison/hybrid approach
self.geometric_slam = VisualInertialSLAM()
# Optimization parameters
self.optimizer = torch.optim.AdamW(
self.neural_slam.parameters(),
lr=1e-4,
weight_decay=1e-4
)
# Mixed precision training for Tensor Core efficiency
self.scaler = torch.cuda.amp.GradScaler()
def train_step(
self,
images: torch.Tensor,
ground_truth_poses: torch.Tensor,
ground_truth_depth: torch.Tensor
) -> Dict[str, float]:
"""Single training step with mixed precision"""
self.neural_slam.train()
# Move data to device
images = images.to(self.device)
gt_poses = ground_truth_poses.to(self.device)
gt_depth = ground_truth_depth.to(self.device)
# Forward pass with mixed precision
with torch.cuda.amp.autocast():
outputs = self.neural_slam(images)
# Compute pose loss
pose_loss = F.mse_loss(outputs['pose'], gt_poses)
# Compute depth loss
depth_loss = F.l1_loss(outputs['depth'], gt_depth)
# Total loss
total_loss = pose_loss + 0.5 * depth_loss
# Backward pass with gradient scaling
self.optimizer.zero_grad()
self.scaler.scale(total_loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
return {
'pose_loss': pose_loss.item(),
'depth_loss': depth_loss.item(),
'total_loss': total_loss.item()
}
def inference(self, image: torch.Tensor) -> Dict[str, torch.Tensor]:
"""Run inference with optimized Tensor Core operations"""
self.neural_slam.eval()
with torch.no_grad():
# Use mixed precision for inference as well
with torch.cuda.amp.autocast():
outputs = self.neural_slam(image.to(self.device))
# Move results back to CPU for further processing
cpu_outputs = {}
for key, value in outputs.items():
cpu_outputs[key] = value.cpu()
return cpu_outputs
def hybrid_slam(
self,
image: torch.Tensor,
imu_data: Dict,
geometric_features: List[FeaturePoint]
) -> SLAMState:
"""Combine neural and geometric SLAM for robust performance"""
# Get neural estimates
neural_outputs = self.inference(image)
neural_pose = neural_outputs['pose']
neural_depth = neural_outputs['depth']
# Get geometric estimates from traditional SLAM
geometric_pose = self.geometric_slam.estimate_pose(geometric_features, imu_data)
# Fuse estimates based on confidence
fused_pose = self.fuse_neural_geometric(
neural_pose, geometric_pose, confidence_threshold=0.7
)
return self.update_slam_state(fused_pose)
def fuse_neural_geometric(
self,
neural_pose: torch.Tensor,
geometric_pose: Dict,
confidence_threshold: float = 0.7
) -> Dict:
"""Fuse neural and geometric pose estimates"""
# Convert neural pose to geometric format
neural_translation = neural_pose[:3].cpu().numpy()
neural_rotation = self.quaternion_from_euler(neural_pose[3:].cpu().numpy())
# Compute confidence scores
neural_confidence = self.compute_neural_confidence(neural_pose)
geometric_confidence = self.compute_geometric_confidence(geometric_pose)
# Weighted fusion based on confidence
if neural_confidence > confidence_threshold:
weight = 0.8 # Trust neural more when confident
elif geometric_confidence > confidence_threshold:
weight = 0.2 # Trust geometric more when features are good
else:
weight = 0.5 # Equal weighting when both uncertain
# Interpolate poses
fused_translation = (
weight * neural_translation +
(1 - weight) * geometric_pose['t']
)
fused_rotation = self.slerp(
neural_rotation,
geometric_pose['R'],
weight
)
return {
'R': fused_rotation,
't': fused_translation,
'confidence': max(neural_confidence, geometric_confidence)
}
def compute_neural_confidence(self, neural_pose: torch.Tensor) -> float:
"""Compute confidence score for neural pose estimate"""
# Use variance of pose predictions or other uncertainty measures
# For now, return a simple heuristic
return float(torch.sigmoid(torch.norm(neural_pose) * 0.1))
def compute_geometric_confidence(self, geometric_pose: Dict) -> float:
"""Compute confidence score for geometric pose estimate"""
# Based on number of tracked features, reprojection error, etc.
return geometric_pose.get('success', False) * 0.9 or 0.1
def quaternion_from_euler(self, euler_angles: np.ndarray) -> np.ndarray:
"""Convert Euler angles to quaternion"""
# Simplified conversion - in practice, use proper Euler to quaternion
roll, pitch, yaw = euler_angles
cy = np.cos(yaw * 0.5)
sy = np.sin(yaw * 0.5)
cp = np.cos(pitch * 0.5)
sp = np.sin(pitch * 0.5)
cr = np.cos(roll * 0.5)
sr = np.sin(roll * 0.5)
w = cr * cp * cy + sr * sp * sy
x = sr * cp * cy - cr * sp * sy
y = cr * sp * cy + sr * cp * sy
z = cr * cp * sy - sr * sp * cy
return np.array([w, x, y, z])
def benchmark_performance(self, num_iterations: int = 100) -> Dict[str, float]:
"""Benchmark Tensor Core SLAM performance"""
# Create dummy input data
dummy_image = torch.randn(1, 3, 480, 640, device=self.device)
dummy_poses = torch.randn(1, 6, device=self.device)
dummy_depth = torch.randn(1, 1, 480, 640, device=self.device)
# Warmup
for _ in range(10):
_ = self.inference(dummy_image)
# Benchmark inference
start_time = time.time()
for _ in range(num_iterations):
_ = self.inference(dummy_image)
inference_time = (time.time() - start_time) / num_iterations
# Benchmark training
start_time = time.time()
for _ in range(num_iterations):
_ = self.train_step(dummy_image, dummy_poses, dummy_depth)
training_time = (time.time() - start_time) / num_iterations
return {
'inference_fps': 1.0 / inference_time,
'inference_time_ms': inference_time * 1000,
'training_time_ms': training_time * 1000,
'tensor_core_utilization': self.get_tensor_core_utilization()
}
def get_tensor_core_utilization(self) -> float:
"""Get Tensor Core utilization (requires proper monitoring)"""
# This would interface with NVIDIA's profiling tools
# For now, return a placeholder
return 0.85 # 85% utilization estimate
Integration with Humanoid Robot Systems
SLAM-Enabled Navigation for Humanoid Robots
Integrating SLAM with humanoid robot navigation requires special consideration for the robot's bipedal nature, height variations, and interaction with humans in shared spaces.
class HumanoidSLAMNavigator:
"""SLAM-enabled navigation system for humanoid robots"""
def __init__(self, slam_system: IsaacSLAMSystem):
self.slam = slam_system
self.path_planner = HumanoidPathPlanner()
self.trajectory_executor = HumanoidTrajectoryExecutor()
self.safety_monitor = HumanoidSafetyMonitor()
# Human-aware navigation components
self.human_detector = HumanDetector()
self.social_navigation = SocialNavigationPlanner()
self.current_map = None
self.robot_pose = None
def update_navigation_system(self):
"""Update navigation system with latest SLAM data"""
# Get current map from SLAM
self.current_map = self.slam.get_current_map()
# Get robot pose from SLAM
self.robot_pose = self.slam.get_robot_pose()
if self.current_map and self.robot_pose:
# Update path planner with new map
self.path_planner.update_map(self.current_map)
# Update safety monitor with current pose
self.safety_monitor.update_pose(self.robot_pose)
def plan_navigation_path(
self,
goal_position: np.ndarray,
avoid_humans: bool = True
) -> List[np.ndarray]:
"""Plan navigation path considering SLAM map and humans"""
if not self.current_map or not self.robot_pose:
raise RuntimeError("SLAM system not ready")
# Detect humans in environment
humans = self.human_detector.detect_humans()
# Plan path with human-aware considerations
if avoid_humans and humans:
path = self.social_navigation.plan_path(
start=self.robot_pose['position'],
goal=goal_position,
humans=humans,
map=self.current_map
)
else:
path = self.path_planner.plan_path(
start=self.robot_pose['position'],
goal=goal_position,
map=self.current_map
)
return path
def execute_navigation(
self,
path: List[np.ndarray],
speed_profile: str = "normal"
) -> bool:
"""Execute navigation path while maintaining SLAM tracking"""
success = True
for waypoint in path:
# Check if SLAM tracking is still good
if not self.slam_tracking_valid():
print("SLAM tracking lost, stopping navigation")
success = False
break
# Execute trajectory to waypoint
trajectory_success = self.trajectory_executor.execute_to_waypoint(
waypoint,
speed_profile
)
if not trajectory_success:
print(f"Failed to reach waypoint {waypoint}")
success = False
break
# Update safety status
if not self.safety_monitor.check_safety():
print("Safety violation detected, stopping")
success = False
break
return success
def slam_tracking_valid(self) -> bool:
"""Check if SLAM tracking is still valid"""
localization_metrics = self.slam.get_localization_metrics()
# Check position accuracy
pos_accuracy = localization_metrics.get('position_accuracy', float('inf'))
if pos_accuracy > 0.5: # 50cm threshold
return False
# Check tracking quality
tracking_quality = localization_metrics.get('tracking_quality', 0.0)
if tracking_quality < 0.5: # 50% threshold
return False
return True
def handle_relocalization(self):
"""Handle SLAM relocalization when tracking is lost"""
print("Attempting SLAM relocalization...")
# Stop robot motion
self.trajectory_executor.stop_motion()
# Try to relocalize using global map
success = self.slam.relocalize()
if success:
print("Relocalization successful")
return True
else:
print("Relocalization failed, requesting human assistance")
return False
class HumanoidPathPlanner:
"""Path planner optimized for humanoid robots"""
def __init__(self):
self.map_resolution = 0.1 # 10cm resolution
self.robot_radius = 0.3 # 30cm radius
self.max_step_height = 0.1 # 10cm max step
self.max_slope = 15.0 # 15 degree max slope
def plan_path(
self,
start: np.ndarray,
goal: np.ndarray,
map: Dict
) -> List[np.ndarray]:
"""Plan path for humanoid robot considering its constraints"""
# Convert to 2D for path planning (ignore Z temporarily)
start_2d = start[:2]
goal_2d = goal[:2]
# Use A* with humanoid-specific cost function
path_2d = self.a_star_humonoid(
start_2d, goal_2d, map, self.robot_radius
)
# Lift path to 3D considering terrain
path_3d = self.lift_path_to_3d(path_2d, map)
return path_3d
def a_star_humonoid(
self,
start: np.ndarray,
goal: np.ndarray,
map: Dict,
robot_radius: float
) -> List[np.ndarray]:
"""A* path planning with humanoid constraints"""
import heapq
# Convert start and goal to grid coordinates
start_grid = self.world_to_grid(start, map)
goal_grid = self.world_to_grid(goal, map)
# Priority queue: (cost, position)
open_set = [(0, start_grid)]
came_from = {}
g_score = {tuple(start_grid): 0}
f_score = {tuple(start_grid): self.heuristic(start_grid, goal_grid)}
while open_set:
current = heapq.heappop(open_set)[1]
if np.array_equal(current, goal_grid):
# Reconstruct path
path = self.reconstruct_path(came_from, current)
return self.grid_to_world_path(path, map)
for neighbor in self.get_neighbors(current, map, robot_radius):
tentative_g_score = g_score[tuple(current)] + self.distance(current, neighbor)
neighbor_key = tuple(neighbor)
if tentative_g_score < g_score.get(neighbor_key, float('inf')):
came_from[neighbor_key] = current
g_score[neighbor_key] = tentative_g_score
f_score[neighbor_key] = tentative_g_score + self.heuristic(neighbor, goal_grid)
# Add to open set if not already there
if not any(np.array_equal(neighbor, item[1]) for item in open_set):
heapq.heappush(open_set, (f_score[neighbor_key], neighbor))
return [] # No path found
def get_neighbors(self, pos: np.ndarray, map: Dict, robot_radius: float) -> List[np.ndarray]:
"""Get valid neighbors considering humanoid constraints"""
neighbors = []
# 8-connected neighborhood
for dx in [-1, 0, 1]:
for dy in [-1, 0, 1]:
if dx == 0 and dy == 0:
continue
neighbor = pos + np.array([dx, dy])
# Check map bounds
if not self.is_in_map_bounds(neighbor, map):
continue
# Check collision (considering robot radius)
if self.is_collision_free(neighbor, map, robot_radius):
# Check step height constraint
if self.is_step_height_valid(pos, neighbor, map):
# Check slope constraint
if self.is_slope_valid(pos, neighbor, map):
neighbors.append(neighbor)
return neighbors
def is_collision_free(self, pos: np.ndarray, map: Dict, robot_radius: float) -> bool:
"""Check if position is collision-free considering robot radius"""
# Check a circular area around the position
grid_radius = int(robot_radius / map['resolution'])
for dx in range(-grid_radius, grid_radius + 1):
for dy in range(-grid_radius, grid_radius + 1):
check_pos = pos + np.array([dx, dy])
if self.is_in_map_bounds(check_pos, map):
grid_idx = tuple(check_pos.astype(int))
if map['occupancy'].get(grid_idx, 1) > 0.5: # Occupied
return False
return True
def is_step_height_valid(self, pos1: np.ndarray, pos2: np.ndarray, map: Dict) -> bool:
"""Check if step height between two positions is acceptable"""
h1 = self.get_height(pos1, map)
h2 = self.get_height(pos2, map)
height_diff = abs(h1 - h2)
return height_diff <= self.max_step_height
def is_slope_valid(self, pos1: np.ndarray, pos2: np.ndarray, map: Dict) -> bool:
"""Check if slope between two positions is acceptable"""
h1 = self.get_height(pos1, map)
h2 = self.get_height(pos2, map)
dist_2d = np.linalg.norm(pos2[:2] - pos1[:2])
if dist_2d == 0:
return True
slope = abs(h2 - h1) / dist_2d
slope_degrees = np.degrees(np.arctan(slope))
return slope_degrees <= self.max_slope
def get_height(self, pos: np.ndarray, map: Dict) -> float:
"""Get terrain height at position"""
grid_pos = pos.astype(int)
grid_key = tuple(grid_pos)
return map['elevation'].get(grid_key, 0.0)
def is_in_map_bounds(self, pos: np.ndarray, map: Dict) -> bool:
"""Check if position is within map bounds"""
min_bounds = map['bounds']['min']
max_bounds = map['bounds']['max']
return (min_bounds[0] <= pos[0] <= max_bounds[0] and
min_bounds[1] <= pos[1] <= max_bounds[1])
def world_to_grid(self, pos: np.ndarray, map: Dict) -> np.ndarray:
"""Convert world coordinates to grid coordinates"""
origin = map['origin']
resolution = map['resolution']
return ((pos - origin[:2]) / resolution).astype(int)
def grid_to_world(self, grid_pos: np.ndarray, map: Dict) -> np.ndarray:
"""Convert grid coordinates to world coordinates"""
origin = map['origin']
resolution = map['resolution']
return grid_pos * resolution + origin[:2]
def grid_to_world_path(self, grid_path: List[np.ndarray], map: Dict) -> List[np.ndarray]:
"""Convert grid path to world coordinates"""
world_path = []
for grid_pos in grid_path:
world_pos = self.grid_to_world(grid_pos, map)
# Add Z coordinate from map
z = self.get_height(grid_pos, map)
world_pos = np.append(world_pos, z)
world_path.append(world_pos)
return world_path
def heuristic(self, pos1: np.ndarray, pos2: np.ndarray) -> float:
"""Heuristic function for A*"""
return np.linalg.norm(pos1 - pos2)
def distance(self, pos1: np.ndarray, pos2: np.ndarray) -> float:
"""Distance function"""
return np.linalg.norm(pos1 - pos2)
def reconstruct_path(self, came_from: Dict, current: np.ndarray) -> List[np.ndarray]:
"""Reconstruct path from A* search"""
path = [current]
current_key = tuple(current)
while current_key in came_from:
current = came_from[current_key]
path.append(current)
current_key = tuple(current)
path.reverse()
return path
def lift_path_to_3d(self, path_2d: List[np.ndarray], map: Dict) -> List[np.ndarray]:
"""Lift 2D path to 3D considering terrain"""
path_3d = []
for pos_2d in path_2d:
z = self.get_height(pos_2d.astype(int), map)
pos_3d = np.array([pos_2d[0], pos_2d[1], z])
path_3d.append(pos_3d)
return path_3d
class HumanoidTrajectoryExecutor:
"""Execute trajectories for humanoid robot navigation"""
def __init__(self):
self.current_trajectory = None
self.execution_status = "idle"
self.speed_profiles = {
"slow": {"max_vel": 0.3, "max_acc": 0.5},
"normal": {"max_vel": 0.6, "max_acc": 1.0},
"fast": {"max_vel": 1.0, "max_acc": 1.5}
}
def execute_to_waypoint(
self,
waypoint: np.ndarray,
speed_profile: str = "normal"
) -> bool:
"""Execute trajectory to reach a waypoint"""
if speed_profile not in self.speed_profiles:
speed_profile = "normal"
profile = self.speed_profiles[speed_profile]
# Generate trajectory to waypoint
trajectory = self.generate_trajectory_to_waypoint(waypoint, profile)
# Execute trajectory
success = self.follow_trajectory(trajectory)
return success
def generate_trajectory_to_waypoint(
self,
waypoint: np.ndarray,
profile: Dict
) -> List[Dict]:
"""Generate trajectory to reach waypoint"""
# This would interface with humanoid robot's motion planning system
# For now, return a simple straight-line trajectory
current_pos = self.get_current_position() # Would get from robot state
# Calculate trajectory points
distance = np.linalg.norm(waypoint - current_pos)
num_points = max(10, int(distance / 0.1)) # 10cm spacing
trajectory = []
for i in range(num_points + 1):
t = i / num_points if num_points > 0 else 0
pos = current_pos + t * (waypoint - current_pos)
trajectory.append({
'position': pos,
'timestamp': time.time() + t * (distance / profile['max_vel']),
'velocity': profile['max_vel'] * (waypoint - current_pos) / distance if distance > 0 else np.zeros(3)
})
return trajectory
def follow_trajectory(self, trajectory: List[Dict]) -> bool:
"""Follow the generated trajectory"""
self.execution_status = "executing"
for point in trajectory:
# Send position command to robot
success = self.send_position_command(point['position'])
if not success:
self.execution_status = "failed"
return False
# Wait for robot to reach position (with timeout)
if not self.wait_for_reach(point['position'], timeout=5.0):
self.execution_status = "timeout"
return False
self.execution_status = "completed"
return True
def get_current_position(self) -> np.ndarray:
"""Get current robot position"""
# This would interface with robot's state estimation
return np.array([0.0, 0.0, 0.0]) # Placeholder
def send_position_command(self, position: np.ndarray) -> bool:
"""Send position command to robot"""
# This would interface with robot's control system
return True # Placeholder
def wait_for_reach(self, target_pos: np.ndarray, timeout: float = 5.0) -> bool:
"""Wait for robot to reach target position"""
start_time = time.time()
while time.time() - start_time < timeout:
current_pos = self.get_current_position()
distance = np.linalg.norm(current_pos - target_pos)
if distance < 0.1: # 10cm tolerance
return True
time.sleep(0.1)
return False # Timeout
def stop_motion(self):
"""Stop robot motion immediately"""
# Send emergency stop command to robot
pass
class HumanoidSafetyMonitor:
"""Safety monitoring for humanoid robot navigation"""
def __init__(self):
self.safety_thresholds = {
'min_distance_to_obstacle': 0.5, # 50cm
'max_angular_velocity': 1.0, # 1 rad/s
'max_linear_velocity': 1.0, # 1 m/s
'max_torque': 100.0 # 100 Nm
}
self.robot_pose = None
self.collision_warning = False
def update_pose(self, pose: Dict):
"""Update with current robot pose"""
self.robot_pose = pose
def check_safety(self) -> bool:
"""Check if current state is safe"""
if not self.robot_pose:
return False
# Check proximity to obstacles
if self.check_obstacle_proximity():
return False
# Check velocity limits
if self.check_velocity_limits():
return False
# Check torque limits
if self.check_torque_limits():
return False
return True
def check_obstacle_proximity(self) -> bool:
"""Check if robot is too close to obstacles"""
# This would interface with proximity sensors
return False # Placeholder
def check_velocity_limits(self) -> bool:
"""Check if velocities are within safe limits"""
# This would check current velocities against thresholds
return False # Placeholder
def check_torque_limits(self) -> bool:
"""Check if torques are within safe limits"""
# This would check current torques against thresholds
return False # Placeholder
class HumanDetector:
"""Detect humans in SLAM map for social navigation"""
def __init__(self):
self.detection_model = self.load_detection_model()
def load_detection_model(self):
"""Load human detection model"""
# This would load a pre-trained model like YOLO or similar
return None # Placeholder
def detect_humans(self) -> List[Dict]:
"""Detect humans in current environment"""
# This would run detection on current camera images
# and return list of human positions and velocities
return [] # Placeholder
class SocialNavigationPlanner:
"""Plan paths that consider human presence and social norms"""
def __init__(self):
self.social_force_model = SocialForceModel()
def plan_path(
self,
start: np.ndarray,
goal: np.ndarray,
humans: List[Dict],
map: Dict
) -> List[np.ndarray]:
"""Plan socially-aware path"""
# Use social force model to consider human movements
social_path = self.social_force_model.plan_with_humans(
start, goal, humans, map
)
return social_path
class SocialForceModel:
"""Implementation of social force model for human-aware navigation"""
def __init__(self):
self.pedestrian_repulsion = 1.0
self.wall_repulsion = 2.0
self.attraction_strength = 1.0
def plan_with_humans(
self,
start: np.ndarray,
goal: np.ndarray,
humans: List[Dict],
map: Dict
) -> List[np.ndarray]:
"""Plan path using social force model"""
# This would implement the social force model algorithm
# which considers repulsive forces from humans and obstacles
# and attractive force toward the goal
# For now, return a simple path with human avoidance
base_path = self.plan_base_path(start, goal, map)
social_path = self.add_human_avoidance(base_path, humans)
return social_path
def plan_base_path(self, start: np.ndarray, goal: np.ndarray, map: Dict) -> List[np.ndarray]:
"""Plan basic path without social considerations"""
# This would call the regular path planner
return [start, goal] # Placeholder
def add_human_avoidance(self, path: List[np.ndarray], humans: List[Dict]) -> List[np.ndarray]:
"""Add human avoidance to path"""
# Modify path to avoid humans based on their predicted movements
return path # Placeholder
Performance Optimization and Evaluation
Benchmarking SLAM Systems
Evaluating SLAM system performance requires comprehensive metrics that assess both accuracy and computational efficiency, especially critical for real-time humanoid robot applications.
import time
from typing import Dict, List, Tuple
import statistics
import matplotlib.pyplot as plt
class SLAMBenchmarkSuite:
"""Comprehensive benchmarking suite for SLAM systems"""
def __init__(self):
self.metrics = {
'accuracy': {},
'efficiency': {},
'robustness': {},
'real_time_performance': {}
}
self.benchmark_results = {}
self.ground_truth_available = False
def run_comprehensive_benchmark(
self,
slam_system,
dataset: List[Dict],
ground_truth: Optional[List[Dict]] = None
) -> Dict[str, float]:
"""Run comprehensive benchmark on SLAM system"""
if ground_truth:
self.ground_truth_available = True
self.align_ground_truth(ground_truth)
print("Starting SLAM benchmarking...")
# Run accuracy benchmarks
accuracy_results = self.benchmark_accuracy(slam_system, dataset, ground_truth)
# Run efficiency benchmarks
efficiency_results = self.benchmark_efficiency(slam_system, dataset)
# Run robustness benchmarks
robustness_results = self.benchmark_robustness(slam_system, dataset)
# Run real-time performance benchmarks
real_time_results = self.benchmark_real_time(slam_system, dataset)
# Compile results
self.benchmark_results = {
'accuracy': accuracy_results,
'efficiency': efficiency_results,
'robustness': robustness_results,
'real_time_performance': real_time_results,
'overall_score': self.calculate_overall_score(
accuracy_results, efficiency_results,
robustness_results, real_time_results
)
}
return self.benchmark_results
def benchmark_accuracy(
self,
slam_system,
dataset: List[Dict],
ground_truth: Optional[List[Dict]]
) -> Dict[str, float]:
"""Benchmark accuracy metrics"""
if not ground_truth:
print("Warning: No ground truth provided for accuracy benchmark")
return {'accuracy_warning': True}
translation_errors = []
rotation_errors = []
absolute_trajectory_errors = []
for i, (data, gt) in enumerate(zip(dataset, ground_truth)):
# Process frame through SLAM
slam_result = slam_system.process_frame(
data['image'], data['imu'], data['timestamp']
)
# Calculate errors against ground truth
if slam_result and 'pose' in slam_result:
gt_pose = gt['pose']
est_pose = slam_result['pose']
# Translation error
trans_error = np.linalg.norm(
np.array(gt_pose['position']) - np.array(est_pose['position'])
)
translation_errors.append(trans_error)
# Rotation error (in degrees)
gt_rot = np.array(gt_pose['orientation']) # quaternion
est_rot = np.array(est_pose['orientation'])
rot_error = self.quaternion_distance(gt_rot, est_rot)
rotation_errors.append(rot_error)
# Calculate accuracy metrics
results = {
'mean_translation_error': statistics.mean(translation_errors) if translation_errors else float('inf'),
'median_translation_error': statistics.median(translation_errors) if translation_errors else float('inf'),
'std_translation_error': statistics.stdev(translation_errors) if len(translation_errors) > 1 else 0,
'max_translation_error': max(translation_errors) if translation_errors else float('inf'),
'mean_rotation_error': statistics.mean(rotation_errors) if rotation_errors else float('inf'),
'ate_translation_median': statistics.median(translation_errors) if translation_errors else float('inf'),
'ate_translation_mean': statistics.mean(translation_errors) if translation_errors else float('inf'),
'ate_translation_rms': np.sqrt(np.mean(np.array(translation_errors)**2)) if translation_errors else float('inf')
}
return results
def quaternion_distance(self, q1: np.ndarray, q2: np.ndarray) -> float:
"""Calculate rotation error between two quaternions in degrees"""
# Calculate the relative quaternion
q_rel = self.quaternion_multiply(self.quaternion_inverse(q1), q2)
# Convert to angle (in radians, then to degrees)
angle = 2 * np.arccos(abs(q_rel[0])) # w component
return np.degrees(angle)
def quaternion_inverse(self, q: np.ndarray) -> np.ndarray:
"""Calculate quaternion inverse"""
return np.array([q[0], -q[1], -q[2], -q[3]]) / np.sum(q**2)
def quaternion_multiply(self, q1: np.ndarray, q2: np.ndarray) -> np.ndarray:
"""Multiply two quaternions"""
w1, x1, y1, z1 = q1
w2, x2, y2, z2 = q2
w = w1*w2 - x1*x2 - y1*y2 - z1*z2
x = w1*x2 + x1*w2 + y1*z2 - z1*y2
y = w1*y2 - x1*z2 + y1*w2 + z1*x2
z = w1*z2 + x1*y2 - y1*x2 + z1*w2
return np.array([w, x, y, z])
def benchmark_efficiency(self, slam_system, dataset: List[Dict]) -> Dict[str, float]:
"""Benchmark computational efficiency"""
processing_times = []
memory_usage = []
for data in dataset:
# Measure processing time
start_time = time.time()
# Process frame
slam_result = slam_system.process_frame(
data['image'], data['imu'], data['timestamp']
)
end_time = time.time()
processing_time = end_time - start_time
processing_times.append(processing_time)
# Measure memory usage (simplified)
memory_usage.append(self.estimate_memory_usage(slam_system))
results = {
'mean_processing_time': statistics.mean(processing_times),
'median_processing_time': statistics.median(processing_times),
'max_processing_time': max(processing_times),
'min_processing_time': min(processing_times),
'processing_time_std': statistics.stdev(processing_times) if len(processing_times) > 1 else 0,
'frames_per_second': 1.0 / statistics.mean(processing_times),
'mean_memory_usage': statistics.mean(memory_usage) if memory_usage else 0,
'peak_memory_usage': max(memory_usage) if memory_usage else 0,
'real_time_factor': np.mean(processing_times) * len(dataset) / self.calculate_dataset_duration(dataset)
}
return results
def estimate_memory_usage(self, slam_system) -> float:
"""Estimate memory usage of SLAM system"""
# This would interface with system memory monitoring
# For now, return a placeholder
return 100.0 # MB
def calculate_dataset_duration(self, dataset: List[Dict]) -> float:
"""Calculate total duration of dataset"""
if not dataset:
return 0.0
return dataset[-1]['timestamp'] - dataset[0]['timestamp']
def benchmark_robustness(self, slam_system, dataset: List[Dict]) -> Dict[str, float]:
"""Benchmark system robustness"""
tracking_success = []
relocalization_success = []
map_consistency = []
for i, data in enumerate(dataset):
# Process frame and check tracking status
slam_result = slam_system.process_frame(
data['image'], data['imu'], data['timestamp']
)
if slam_result:
tracking_success.append(slam_result.get('tracking_status', 'LOST') == 'OK')
# Check for relocalization events
if slam_result.get('relocalization_performed', False):
relocalization_success.append(slam_result.get('relocalization_success', False))
# Check map consistency
map_quality = slam_result.get('map_quality', 0.0)
map_consistency.append(map_quality)
results = {
'tracking_success_rate': sum(tracking_success) / len(tracking_success) if tracking_success else 0.0,
'relocalization_success_rate': sum(relocalization_success) / len(relocalization_success) if relocalization_success else 0.0,
'mean_map_consistency': statistics.mean(map_consistency) if map_consistency else 0.0,
'map_coverage': self.calculate_map_coverage(slam_system),
'feature_stability': self.calculate_feature_stability(slam_system),
'loop_closure_success_rate': self.calculate_loop_closure_success(slam_system)
}
return results
def calculate_map_coverage(self, slam_system) -> float:
"""Calculate map coverage metric"""
# This would calculate the percentage of environment mapped
return 0.7 # Placeholder
def calculate_feature_stability(self, slam_system) -> float:
"""Calculate feature tracking stability"""
# This would measure how consistently features are tracked
return 0.85 # Placeholder
def calculate_loop_closure_success(self, slam_system) -> float:
"""Calculate loop closure success rate"""
# This would measure how often loop closures are successfully detected and optimized
return 0.9 # Placeholder
def benchmark_real_time(self, slam_system, dataset: List[Dict]) -> Dict[str, float]:
"""Benchmark real-time performance"""
# Simulate real-time execution
real_time_performance = []
deadline_misses = 0
for i, data in enumerate(dataset):
# Simulate real-time constraints
expected_interval = 1.0 / 30.0 # 30 FPS
start_time = time.time()
# Process frame
slam_result = slam_system.process_frame(
data['image'], data['imu'], data['timestamp']
)
end_time = time.time()
processing_time = end_time - start_time
# Check if deadline was met
if processing_time > expected_interval:
deadline_misses += 1
real_time_performance.append({
'processing_time': processing_time,
'deadline_met': processing_time <= expected_interval,
'latency': processing_time
})
results = {
'deadline_success_rate': 1.0 - (deadline_misses / len(dataset)),
'mean_latency': statistics.mean([r['processing_time'] for r in real_time_performance]),
'max_latency': max([r['processing_time'] for r in real_time_performance]),
'latency_jitter': statistics.stdev([r['processing_time'] for r in real_time_performance]),
'real_time_compliance': self.calculate_real_time_compliance(real_time_performance),
'throughput': len(dataset) / self.calculate_dataset_duration(dataset)
}
return results
def calculate_real_time_compliance(self, performance_data: List[Dict]) -> float:
"""Calculate real-time compliance metric"""
compliant_frames = sum(1 for data in performance_data if data['deadline_met'])
return compliant_frames / len(performance_data) if performance_data else 0.0
def calculate_overall_score(
self,
accuracy: Dict,
efficiency: Dict,
robustness: Dict,
real_time: Dict
) -> float:
"""Calculate overall SLAM performance score"""
# Weighted combination of metrics
accuracy_score = self.normalize_accuracy_score(accuracy)
efficiency_score = self.normalize_efficiency_score(efficiency)
robustness_score = self.normalize_robustness_score(robustness)
real_time_score = self.normalize_real_time_score(real_time)
# Weighted average (adjust weights based on application requirements)
weights = {
'accuracy': 0.3,
'efficiency': 0.25,
'robustness': 0.25,
'real_time': 0.2
}
overall_score = (
weights['accuracy'] * accuracy_score +
weights['efficiency'] * efficiency_score +
weights['robustness'] * robustness_score +
weights['real_time'] * real_time_score
)
return overall_score
def normalize_accuracy_score(self, accuracy: Dict) -> float:
"""Normalize accuracy metrics to 0-1 scale"""
if 'mean_translation_error' in accuracy:
# Lower error = higher score
error = accuracy['mean_translation_error']
# Normalize assuming 0.1m is excellent, 1.0m is poor
score = max(0, min(1, (1.0 - error) / 0.9)) if error <= 1.0 else 0.0
else:
score = 0.5 # Unknown
return score
def normalize_efficiency_score(self, efficiency: Dict) -> float:
"""Normalize efficiency metrics to 0-1 scale"""
fps = efficiency.get('frames_per_second', 0)
# Target 30 FPS for real-time operation
score = min(1.0, fps / 30.0) if fps > 0 else 0.0
return score
def normalize_robustness_score(self, robustness: Dict) -> float:
"""Normalize robustness metrics to 0-1 scale"""
tracking_rate = robustness.get('tracking_success_rate', 0.0)
return tracking_rate
def normalize_real_time_score(self, real_time: Dict) -> float:
"""Normalize real-time performance to 0-1 scale"""
compliance = real_time.get('deadline_success_rate', 0.0)
return compliance
def generate_benchmark_report(self, results: Dict) -> str:
"""Generate comprehensive benchmark report"""
report = "SLAM BENCHMARK REPORT\n"
report += "=" * 50 + "\n\n"
report += "ACCURACY METRICS:\n"
for key, value in results['accuracy'].items():
report += f" {key}: {value}\n"
report += "\n"
report += "EFFICIENCY METRICS:\n"
for key, value in results['efficiency'].items():
report += f" {key}: {value}\n"
report += "\n"
report += "ROBUSTNESS METRICS:\n"
for key, value in results['robustness'].items():
report += f" {key}: {value}\n"
report += "\n"
report += "REAL-TIME PERFORMANCE:\n"
for key, value in results['real_time_performance'].items():
report += f" {key}: {value}\n"
report += "\n"
report += f"OVERALL SCORE: {results['overall_score']:.3f}\n"
return report
def plot_benchmark_results(self, results: Dict, save_path: str = None):
"""Plot benchmark results"""
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
# Accuracy plot
accuracy_metrics = list(results['accuracy'].keys())[:5] # First 5 metrics
accuracy_values = list(results['accuracy'].values())[:5]
axes[0, 0].bar(range(len(accuracy_metrics)), accuracy_values)
axes[0, 0].set_title('Accuracy Metrics')
axes[0, 0].set_xticks(range(len(accuracy_metrics)))
axes[0, 0].set_xticklabels(accuracy_metrics, rotation=45, ha='right')
# Efficiency plot
efficiency_metrics = list(results['efficiency'].keys())[:5]
efficiency_values = list(results['efficiency'].values())[:5]
axes[0, 1].bar(range(len(efficiency_metrics)), efficiency_values)
axes[0, 1].set_title('Efficiency Metrics')
axes[0, 1].set_xticks(range(len(efficiency_metrics)))
axes[0, 1].set_xticklabels(efficiency_metrics, rotation=45, ha='right')
# Robustness plot
robustness_metrics = list(results['robustness'].keys())[:5]
robustness_values = list(results['robustness'].values())[:5]
axes[1, 0].bar(range(len(robustness_metrics)), robustness_values)
axes[1, 0].set_title('Robustness Metrics')
axes[1, 0].set_xticks(range(len(robustness_metrics)))
axes[1, 0].set_xticklabels(robustness_metrics, rotation=45, ha='right')
# Real-time performance plot
real_time_metrics = list(results['real_time_performance'].keys())[:5]
real_time_values = list(results['real_time_performance'].values())[:5]
axes[1, 1].bar(range(len(real_time_metrics)), real_time_values)
axes[1, 1].set_title('Real-time Performance')
axes[1, 1].set_xticks(range(len(real_time_metrics)))
axes[1, 1].set_xticklabels(real_time_metrics, rotation=45, ha='right')
plt.tight_layout()
if save_path:
plt.savefig(save_path)
plt.show()
class SLAMOptimizer:
"""Optimization framework for SLAM systems"""
def __init__(self, slam_system):
self.slam = slam_system
self.param_ranges = self.define_parameter_ranges()
self.optimization_history = []
def define_parameter_ranges(self) -> Dict:
"""Define ranges for SLAM parameters to optimize"""
return {
'feature_detector_threshold': (0.001, 0.01),
'max_features': (500, 2000),
'tracking_threshold': (5, 20),
'relocalization_threshold': (20, 100),
'bundle_adjustment_frequency': (5, 50),
'loop_closure_threshold': (0.1, 1.0),
'map_merging_threshold': (0.5, 2.0)
}
def optimize_parameters(
self,
dataset: List[Dict],
ground_truth: List[Dict],
num_iterations: int = 50
) -> Dict:
"""Optimize SLAM parameters using Bayesian optimization"""
from skopt import gp_minimize
from skopt.space import Real, Integer
# Define search space
dimensions = []
for param_name, (low, high) in self.param_ranges.items():
if isinstance(low, int):
dimensions.append(Integer(low, high, name=param_name))
else:
dimensions.append(Real(low, high, name=param_name))
# Objective function
def objective(params):
# Set parameters in SLAM system
param_dict = {}
for i, param_name in enumerate(self.param_ranges.keys()):
param_dict[param_name] = params[i]
self.set_slam_parameters(param_dict)
# Evaluate performance
benchmark = SLAMBenchmarkSuite()
results = benchmark.run_comprehensive_benchmark(
self.slam, dataset, ground_truth
)
# Return negative of overall score (minimize negative = maximize positive)
return -results['overall_score']
# Run optimization
res = gp_minimize(
func=objective,
dimensions=dimensions,
n_calls=num_iterations,
random_state=42
)
# Set optimal parameters
optimal_params = {}
for i, param_name in enumerate(self.param_ranges.keys()):
optimal_params[param_name] = res.x[i]
self.set_slam_parameters(optimal_params)
return {
'optimal_parameters': optimal_params,
'optimal_score': -res.fun,
'optimization_history': res.func_vals
}
def set_slam_parameters(self, params: Dict):
"""Set parameters in SLAM system"""
# This would interface with the specific SLAM system
# to set the optimized parameters
pass
def adaptive_parameter_tuning(self, current_metrics: Dict):
"""Adapt SLAM parameters based on current performance metrics"""
adjustments = {}
# Adjust feature detection based on tracking quality
if current_metrics.get('tracking_success_rate', 1.0) < 0.8:
adjustments['feature_detector_threshold'] = 'decrease' # Detect more features
adjustments['max_features'] = 'increase'
# Adjust optimization frequency based on computational load
if current_metrics.get('mean_processing_time', 0.1) > 0.033: # >30 FPS
adjustments['bundle_adjustment_frequency'] = 'decrease'
adjustments['loop_closure_frequency'] = 'decrease'
# Adjust relocalization sensitivity based on failure rate
if current_metrics.get('relocalization_success_rate', 1.0) < 0.7:
adjustments['relocalization_threshold'] = 'decrease' # Be more sensitive
return adjustments
Troubleshooting Common SLAM Issues
Diagnosing and Resolving SLAM Problems
SLAM systems can encounter various issues that affect performance. Understanding these problems and their solutions is crucial for maintaining robust operation.
class SLAMTroubleshooter:
"""Diagnostic and troubleshooting system for SLAM issues"""
def __init__(self):
self.diagnostic_rules = self.create_diagnostic_rules()
self.solution_database = self.create_solution_database()
self.common_issues = self.identify_common_issues()
def create_diagnostic_rules(self) -> Dict[str, Dict]:
"""Create diagnostic rules for SLAM problems"""
return {
'tracking_failure': {
'conditions': [
'low_feature_count < 50',
'high_reprojection_error > 5.0',
'tracking_status == "LOST"'
],
'severity': 'high',
'symptoms': ['drift', 'position_inaccuracy', 'map_inconsistency']
},
'loop_closure_failure': {
'conditions': [
'low_recognition_rate < 0.1',
'high_descriptor_distance > 0.8',
'loop_closure_attempts > 100'
],
'severity': 'medium',
'symptoms': ['map_drift', 'inconsistent_localization']
},
'map_quality_degradation': {
'conditions': [
'high_map_uncertainty > 0.5',
'low_feature_visibility < 0.3',
'high_reprojection_error > 3.0'
],
'severity': 'medium',
'symptoms': ['poor_navigation', 'localization_drift']
},
'real_time_performance': {
'conditions': [
'processing_time > 0.033', # >30 FPS
'deadline_misses > 10',
'memory_usage > 2000' # MB
],
'severity': 'high',
'symptoms': ['lag', 'frame_drops', 'system_instability']
}
}
def create_solution_database(self) -> Dict[str, List[str]]:
"""Create database of solutions for SLAM issues"""
return {
'tracking_failure': [
"Increase feature detection threshold",
"Improve lighting conditions",
"Reduce camera motion speed",
"Check camera calibration",
"Verify IMU functionality"
],
'loop_closure_failure': [
"Improve map texture richness",
"Adjust loop closure detection parameters",
"Increase descriptor matching threshold",
"Verify geometric verification settings",
"Check for repetitive environments"
],
'map_quality_degradation': [
"Reduce bundle adjustment frequency",
"Increase feature tracking window",
"Improve sensor fusion parameters",
"Verify camera-IMU calibration",
"Check for sensor drift"
],
'real_time_performance': [
"Reduce feature count",
"Use lower resolution images",
"Optimize algorithm parameters",
"Upgrade hardware if necessary",
"Reduce optimization frequency"
],
'drift_accumulation': [
"Enable loop closure detection",
"Improve bundle adjustment settings",
"Verify IMU calibration",
"Check for systematic errors",
"Implement relocalization"
],
'occlusion_handling': [
"Use multiple camera viewpoints",
"Implement temporal consistency checks",
"Improve feature descriptor quality",
"Use semantic information",
"Implement prediction models"
]
}
def diagnose_slam_state(self, slam_metrics: Dict) -> Dict[str, Any]:
"""Diagnose SLAM system state and identify issues"""
issues_found = []
# Check for tracking failure
if self.detect_tracking_failure(slam_metrics):
issues_found.append({
'issue': 'tracking_failure',
'severity': 'high',
'confidence': self.assess_tracking_confidence(slam_metrics)
})
# Check for loop closure issues
if self.detect_loop_closure_issues(slam_metrics):
issues_found.append({
'issue': 'loop_closure_failure',
'severity': 'medium',
'confidence': self.assess_loop_confidence(slam_metrics)
})
# Check for map quality issues
if self.detect_map_quality_issues(slam_metrics):
issues_found.append({
'issue': 'map_quality_degradation',
'severity': 'medium',
'confidence': self.assess_map_confidence(slam_metrics)
})
# Check for performance issues
if self.detect_performance_issues(slam_metrics):
issues_found.append({
'issue': 'real_time_performance',
'severity': 'high',
'confidence': self.assess_performance_confidence(slam_metrics)
})
return {
'issues_found': issues_found,
'diagnosis_confidence': self.calculate_diagnosis_confidence(issues_found),
'recommended_solutions': self.get_solutions_for_issues(issues_found),
'immediate_actions': self.get_immediate_actions(issues_found)
}
def detect_tracking_failure(self, metrics: Dict) -> bool:
"""Detect tracking failure conditions"""
feature_count = metrics.get('current_features', 0)
reprojection_error = metrics.get('mean_reprojection_error', float('inf'))
tracking_status = metrics.get('tracking_status', 'OK')
return (feature_count < 50 or
reprojection_error > 5.0 or
tracking_status == 'LOST')
def detect_loop_closure_issues(self, metrics: Dict) -> bool:
"""Detect loop closure problems"""
recognition_rate = metrics.get('loop_closure_recognition_rate', 1.0)
attempts = metrics.get('loop_closure_attempts', 0)
success_rate = metrics.get('loop_closure_success_rate', 1.0)
return (recognition_rate < 0.1 or
attempts > 100 or
success_rate < 0.3)
def detect_map_quality_issues(self, metrics: Dict) -> bool:
"""Detect map quality degradation"""
uncertainty = metrics.get('map_uncertainty', 0.0)
visibility = metrics.get('feature_visibility_ratio', 1.0)
reprojection_error = metrics.get('mean_reprojection_error', 0.0)
return (uncertainty > 0.5 or
visibility < 0.3 or
reprojection_error > 3.0)
def detect_performance_issues(self, metrics: Dict) -> bool:
"""Detect performance bottlenecks"""
processing_time = metrics.get('mean_processing_time', 0.0)
deadline_misses = metrics.get('deadline_misses', 0)
memory_usage = metrics.get('current_memory_usage', 0)
return (processing_time > 0.033 or # >30 FPS
deadline_misses > 10 or
memory_usage > 2000) # MB
def assess_tracking_confidence(self, metrics: Dict) -> float:
"""Assess confidence in tracking diagnosis"""
feature_count = metrics.get('current_features', 0)
reprojection_error = metrics.get('mean_reprojection_error', float('inf'))
# Higher confidence if multiple indicators present
confidence = 0.0
if feature_count < 30:
confidence += 0.4
if reprojection_error > 10.0:
confidence += 0.3
if metrics.get('tracking_status') == 'LOST':
confidence += 0.3
return min(confidence, 1.0)
def assess_loop_confidence(self, metrics: Dict) -> float:
"""Assess confidence in loop closure diagnosis"""
recognition_rate = metrics.get('loop_closure_recognition_rate', 1.0)
success_rate = metrics.get('loop_closure_success_rate', 1.0)
return 1.0 - min(recognition_rate, success_rate)
def assess_map_confidence(self, metrics: Dict) -> float:
"""Assess confidence in map quality diagnosis"""
uncertainty = metrics.get('map_uncertainty', 0.0)
visibility = metrics.get('feature_visibility_ratio', 1.0)
return max(uncertainty, 1.0 - visibility)
def assess_performance_confidence(self, metrics: Dict) -> float:
"""Assess confidence in performance diagnosis"""
processing_time = metrics.get('mean_processing_time', 0.0)
deadline_rate = metrics.get('deadline_success_rate', 1.0)
time_pressure = processing_time / 0.033 # Normalize to 30 FPS target
return max(time_pressure, 1.0 - deadline_rate)
def calculate_diagnosis_confidence(self, issues: List[Dict]) -> float:
"""Calculate overall diagnosis confidence"""
if not issues:
return 0.0
total_confidence = sum(issue['confidence'] for issue in issues)
return total_confidence / len(issues)
def get_solutions_for_issues(self, issues: List[Dict]) -> List[str]:
"""Get solutions for identified issues"""
all_solutions = []
for issue in issues:
issue_type = issue['issue']
if issue_type in self.solution_database:
all_solutions.extend(self.solution_database[issue_type])
# Remove duplicates while preserving order
return list(dict.fromkeys(all_solutions))
def get_immediate_actions(self, issues: List[Dict]) -> List[str]:
"""Get immediate actions for critical issues"""
immediate_actions = []
for issue in issues:
if issue['severity'] == 'high':
if issue['issue'] == 'tracking_failure':
immediate_actions.extend([
"Initiate relocalization procedure",
"Reduce robot motion speed",
"Switch to safe recovery mode"
])
elif issue['issue'] == 'real_time_performance':
immediate_actions.extend([
"Reduce feature processing load",
"Switch to simplified tracking mode",
"Alert system operator"
])
return immediate_actions
def run_preventive_checks(self, slam_system) -> Dict[str, bool]:
"""Run preventive checks to avoid SLAM issues"""
checks = {
'camera_calibration_valid': self.check_camera_calibration(slam_system),
'imu_functionality': self.check_imu_functionality(slam_system),
'sensor_synchronization': self.check_sensor_sync(slam_system),
'environment_suitability': self.check_environment_texture(slam_system),
'computational_resources': self.check_resources_available(slam_system)
}
return checks
def check_camera_calibration(self, slam_system) -> bool:
"""Check if camera calibration is valid"""
# This would verify camera intrinsics and extrinsics
return True # Placeholder
def check_imu_functionality(self, slam_system) -> bool:
"""Check if IMU is functioning properly"""
# This would verify IMU data quality and calibration
return True # Placeholder
def check_sensor_sync(self, slam_system) -> bool:
"""Check if sensors are properly synchronized"""
# This would verify timestamp alignment between sensors
return True # Placeholder
def check_environment_texture(self, slam_system) -> bool:
"""Check if environment has sufficient texture for SLAM"""
# This would analyze image for feature richness
return True # Placeholder
def check_resources_available(self, slam_system) -> bool:
"""Check if sufficient computational resources are available"""
# This would verify CPU, GPU, and memory availability
return True # Placeholder
def generate_troubleshooting_report(self, diagnosis: Dict) -> str:
"""Generate troubleshooting report"""
report = "SLAM TROUBLESHOOTING REPORT\n"
report += "=" * 50 + "\n\n"
report += f"Diagnosis Confidence: {diagnosis['diagnosis_confidence']:.2f}\n\n"
report += "ISSUES IDENTIFIED:\n"
for issue in diagnosis['issues_found']:
report += f"- {issue['issue']} (Severity: {issue['severity']}, Confidence: {issue['confidence']:.2f})\n"
report += "\n"
report += "RECOMMENDED SOLUTIONS:\n"
for solution in diagnosis['recommended_solutions']:
report += f"- {solution}\n"
report += "\n"
if diagnosis['immediate_actions']:
report += "IMMEDIATE ACTIONS REQUIRED:\n"
for action in diagnosis['immediate_actions']:
report += f"- {action}\n"
return report
class SLAMHealthMonitor:
"""Continuous monitoring system for SLAM health"""
def __init__(self):
self.troubleshooter = SLAMTroubleshooter()
self.monitoring_history = []
self.alert_thresholds = self.set_alert_thresholds()
def set_alert_thresholds(self) -> Dict[str, float]:
"""Set thresholds for different alert levels"""
return {
'tracking_quality': 0.5, # Below this is problematic
'map_accuracy': 0.1, # Above this error is problematic
'processing_time': 0.033, # 30 FPS threshold
'feature_count': 50, # Minimum features
'memory_usage': 0.8, # 80% of available memory
'relocalization_rate': 0.1 # 10% relocalization is concerning
}
def monitor_slam_health(self, slam_metrics: Dict) -> Dict[str, Any]:
"""Monitor SLAM system health and generate alerts"""
# Run diagnostic
diagnosis = self.troubleshooter.diagnose_slam_state(slam_metrics)
# Check individual metrics against thresholds
alerts = self.check_metric_thresholds(slam_metrics)
# Generate health report
health_status = {
'diagnosis': diagnosis,
'alerts': alerts,
'overall_health': self.calculate_overall_health(slam_metrics, alerts),
'trend_analysis': self.analyze_trends(slam_metrics),
'maintenance_recommendations': self.get_maintenance_recommendations(slam_metrics)
}
# Store for trend analysis
self.monitoring_history.append({
'timestamp': time.time(),
'metrics': slam_metrics,
'health_status': health_status
})
return health_status
def check_metric_thresholds(self, metrics: Dict) -> List[Dict]:
"""Check metrics against alert thresholds"""
alerts = []
# Check tracking quality
if metrics.get('tracking_quality', 1.0) < self.alert_thresholds['tracking_quality']:
alerts.append({
'type': 'tracking_quality_low',
'severity': 'warning',
'value': metrics.get('tracking_quality'),
'threshold': self.alert_thresholds['tracking_quality']
})
# Check map accuracy
if metrics.get('position_accuracy', 0.0) > self.alert_thresholds['map_accuracy']:
alerts.append({
'type': 'map_accuracy_poor',
'severity': 'warning',
'value': metrics.get('position_accuracy'),
'threshold': self.alert_thresholds['map_accuracy']
})
# Check processing time
if metrics.get('mean_processing_time', 0.0) > self.alert_thresholds['processing_time']:
alerts.append({
'type': 'performance_degraded',
'severity': 'warning',
'value': metrics.get('mean_processing_time'),
'threshold': self.alert_thresholds['processing_time']
})
# Check feature count
if metrics.get('current_features', 100) < self.alert_thresholds['feature_count']:
alerts.append({
'type': 'feature_starvation',
'severity': 'warning',
'value': metrics.get('current_features'),
'threshold': self.alert_thresholds['feature_count']
})
return alerts
def calculate_overall_health(self, metrics: Dict, alerts: List[Dict]) -> float:
"""Calculate overall SLAM health score (0-1 scale)"""
# Start with perfect health
health_score = 1.0
# Deduct for each alert
for alert in alerts:
if alert['severity'] == 'warning':
health_score -= 0.1
elif alert['severity'] == 'error':
health_score -= 0.3
# Additional deductions based on key metrics
tracking_quality = metrics.get('tracking_quality', 1.0)
health_score *= tracking_quality
processing_time = metrics.get('mean_processing_time', 0.0)
if processing_time > 0.1: # 10 FPS
health_score *= 0.5
elif processing_time > 0.05: # 20 FPS
health_score *= 0.8
return max(0.0, min(1.0, health_score))
def analyze_trends(self, current_metrics: Dict) -> Dict[str, float]:
"""Analyze trends in SLAM performance over time"""
if len(self.monitoring_history) < 10:
return {'trend_analysis_pending': True}
# Calculate trends for key metrics
recent_metrics = self.monitoring_history[-10:]
trends = {}
# Analyze processing time trend
proc_times = [m['metrics'].get('mean_processing_time', 0) for m in recent_metrics]
if len(proc_times) > 1:
time_trend = (proc_times[-1] - proc_times[0]) / len(proc_times)
trends['processing_time_trend'] = time_trend
# Analyze tracking quality trend
track_qualities = [m['metrics'].get('tracking_quality', 1.0) for m in recent_metrics]
if len(track_qualities) > 1:
quality_trend = (track_qualities[-1] - track_qualities[0]) / len(track_qualities)
trends['tracking_quality_trend'] = quality_trend
# Analyze feature count trend
feature_counts = [m['metrics'].get('current_features', 100) for m in recent_metrics]
if len(feature_counts) > 1:
feature_trend = (feature_counts[-1] - feature_counts[0]) / len(feature_counts)
trends['feature_count_trend'] = feature_trend
return trends
def get_maintenance_recommendations(self, metrics: Dict) -> List[str]:
"""Get maintenance recommendations based on current state"""
recommendations = []
# Based on current metrics, suggest maintenance actions
if metrics.get('tracking_quality', 1.0) < 0.7:
recommendations.append("Consider recalibrating sensors")
if metrics.get('mean_processing_time', 0.0) > 0.05:
recommendations.append("Review algorithm parameters for optimization")
if metrics.get('relocalization_count', 0) > 5:
recommendations.append("Investigate environmental factors causing relocalizations")
if metrics.get('loop_closure_success_rate', 1.0) < 0.8:
recommendations.append("Verify loop closure parameters and environment texture")
return recommendations
def get_health_summary(self) -> Dict[str, Any]:
"""Get summary of current SLAM health status"""
if not self.monitoring_history:
return {'status': 'no_data', 'message': 'No monitoring data available'}
latest = self.monitoring_history[-1]
return {
'status': 'monitoring_active',
'timestamp': latest['timestamp'],
'overall_health_score': latest['health_status']['overall_health'],
'active_alerts': len(latest['health_status']['alerts']),
'diagnosed_issues': len(latest['health_status']['diagnosis']['issues_found']),
'last_update': time.time() - latest['timestamp']
}
Summary
Hardware-Accelerated Visual-Inertial SLAM represents a critical technology for enabling autonomous humanoid robots to navigate and understand their environment effectively. This chapter has covered comprehensive approaches to implementing SLAM systems optimized for NVIDIA's hardware platforms:
Key Techniques Covered:
-
NVIDIA Isaac SLAM Integration: Leveraging Isaac Sim's sensor simulation and SLAM capabilities for humanoid robot applications.
-
Omniverse Replicator: Generating synthetic training data with photorealistic quality to train robust SLAM systems that can handle diverse environments.
-
GPU Acceleration: Utilizing CUDA cores and Tensor Cores for real-time feature detection, tracking, and optimization.
-
Neural SLAM: Combining traditional geometric methods with deep learning for enhanced performance and robustness.
-
Humanoid-Specific Navigation: Adapting SLAM for the unique challenges of bipedal robots, including height variations and human-aware navigation.
-
Performance Optimization: Techniques for achieving real-time performance while maintaining accuracy.
Hardware Optimization:
The chapter emphasized the importance of leveraging NVIDIA's specialized hardware for maximum performance, including:
- GPU-accelerated feature detection and matching
- Tensor Core optimization for neural components
- Memory management for real-time processing
- Mixed precision training for efficiency
Evaluation and Monitoring:
Comprehensive benchmarking frameworks were presented to evaluate SLAM system performance across accuracy, efficiency, robustness, and real-time compliance metrics. Continuous health monitoring systems help maintain optimal performance and detect issues before they become critical.
Next Steps
In the next chapter, we'll explore the integration of all these AI components into a cohesive AI-Robot Brain architecture, focusing on how to orchestrate perception, planning, control, and learning systems for humanoid robots. We'll cover system integration patterns, real-time scheduling, and how to create a unified cognitive architecture for humanoid robots.
Estimated Reading Time: 35 minutes