VSLAM in Humanoid Robotics
Visual Simultaneous Localization and Mapping (VSLAM) is a critical capability for humanoid robots to navigate and understand their environment. VSLAM allows robots to build a map of an unknown environment while simultaneously tracking their position within that map using visual sensors.
Introduction to VSLAM
VSLAM is essential for humanoid robots because:
- It enables autonomous navigation in unknown environments
- It provides spatial understanding without pre-existing maps
- It uses commonly available visual sensors (cameras)
- It allows for drift correction through loop closure detection
VSLAM Pipeline Components
Feature Detection and Tracking
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
import numpy as np
from collections import deque
class FeatureTracker(Node):
def __init__(self):
super().__init__('feature_tracker')
# Subscribe to camera feed
self.image_sub = self.create_subscription(
Image,
'/camera/image',
self.image_callback,
10
)
self.bridge = CvBridge()
# Feature tracking parameters
self.feature_params = dict(
maxCorners=100,
qualityLevel=0.3,
minDistance=7,
blockSize=7
)
self.lk_params = dict(
winSize=(15, 15),
maxLevel=2,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)
)
# Store previous frame and features
self.prev_gray = None
self.prev_features = None
self.track_len = 10
self.tracks = deque(maxlen=self.track_len)
self.get_logger().info('Feature Tracker initialized')
def image_callback(self, msg):
"""Process incoming images to track features"""
# Convert ROS Image to OpenCV
curr_frame = self.bridge.imgmsg_to_cv2(msg, desired_encoding='mono8')
if self.prev_gray is None:
# Initialize first frame
self.prev_gray = curr_frame
self.prev_features = cv2.goodFeaturesToTrack(
self.prev_gray,
**self.feature_params
)
return
# Calculate optical flow
curr_features, status, err = cv2.calcOpticalFlowPyrLK(
self.prev_gray, curr_frame,
self.prev_features, None,
**self.lk_params
)
# Filter good features
good_curr = curr_features[status == 1]
good_prev = self.prev_features[status == 1]
# Add to tracks
for i, (curr, prev) in enumerate(zip(good_curr, good_prev)):
self.tracks.append((curr, prev))
# Update for next iteration
self.prev_gray = curr_frame.copy()
self.prev_features = good_curr.reshape(-1, 1, 2)
Pose Estimation
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import PoseStamped, TwistStamped
from std_msgs.msg import Float32
import numpy as np
from scipy.spatial.transform import Rotation as R
class PoseEstimator(Node):
def __init__(self):
super().__init__('pose_estimator')
# Publisher for pose estimates
self.pose_pub = self.create_publisher(PoseStamped, '/vslam/pose', 10)
self.twist_pub = self.create_publisher(TwistStamped, '/vslam/twist', 10)
# Initialize pose
self.current_pose = np.eye(4) # 4x4 homogeneous transformation matrix
self.prev_features = None
self.camera_matrix = np.array([[554.256, 0.0, 320.5],
[0.0, 554.256, 240.5],
[0.0, 0.0, 1.0]])
# For velocity estimation
self.prev_time = None
self.prev_position = np.array([0.0, 0.0, 0.0])
self.get_logger().info('Pose Estimator initialized')
def estimate_pose(self, prev_points, curr_points):
"""Estimate relative pose change from tracked features"""
if len(prev_points) < 8 or len(curr_points) < 8:
return np.eye(4) # Return identity if not enough points
# Estimate essential matrix
E, mask = cv2.findEssentialMat(
curr_points, prev_points,
self.camera_matrix,
method=cv2.RANSAC,
prob=0.999,
threshold=1.0
)
if E is None or len(E) == 0:
return np.eye(4)
# Decompose essential matrix to get rotation and translation
_, R, t, _ = cv2.recoverPose(E, curr_points, prev_points, self.camera_matrix)
# Create transformation matrix
transformation = np.eye(4)
transformation[:3, :3] = R
transformation[:3, 3] = t.flatten()
return transformation
def update_pose(self, relative_transform):
"""Update the global pose with relative transformation"""
# Apply the relative transformation to current pose
self.current_pose = np.dot(self.current_pose, relative_transform)
# Publish the updated pose
self.publish_pose()
def publish_pose(self):
"""Publish the current pose"""
pose_msg = PoseStamped()
pose_msg.header.stamp = self.get_clock().now().to_msg()
pose_msg.header.frame_id = 'map'
# Extract position
position = self.current_pose[:3, 3]
pose_msg.pose.position.x = float(position[0])
pose_msg.pose.position.y = float(position[1])
pose_msg.pose.position.z = float(position[2])
# Extract orientation (convert rotation matrix to quaternion)
rotation_matrix = self.current_pose[:3, :3]
r = R.from_matrix(rotation_matrix)
quat = r.as_quat()
pose_msg.pose.orientation.x = float(quat[0])
pose_msg.pose.orientation.y = float(quat[1])
pose_msg.pose.orientation.z = float(quat[2])
pose_msg.pose.orientation.w = float(quat[3])
self.pose_pub.publish(pose_msg)
# Also publish velocity if we have previous data
self.publish_velocity()
def publish_velocity(self):
"""Publish estimated linear velocity"""
current_time = self.get_clock().now()
current_position = self.current_pose[:3, 3]
if self.prev_time is not None:
dt = (current_time - self.prev_time).nanoseconds / 1e9
if dt > 0:
velocity = (current_position - self.prev_position) / dt
twist_msg = TwistStamped()
twist_msg.header.stamp = current_time.to_msg()
twist_msg.header.frame_id = 'base_link'
twist_msg.twist.linear.x = float(velocity[0])
twist_msg.twist.linear.y = float(velocity[1])
twist_msg.twist.linear.z = float(velocity[2])
self.twist_pub.publish(twist_msg)
self.prev_time = current_time
self.prev_position = current_position
Mapping Component
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import PointCloud2
from geometry_msgs.msg import PointStamped
from visualization_msgs.msg import MarkerArray
from std_msgs.msg import Header
import numpy as np
import struct
class VSLAMMap(Node):
def __init__(self):
super().__init__('vslam_map')
# Publisher for map representation
self.map_pub = self.create_publisher(PointCloud2, '/vslam/map', 10)
self.landmarks_pub = self.create_publisher(MarkerArray, '/vslam/landmarks', 10)
# Store landmarks
self.landmarks = {}
self.landmark_id_counter = 0
# For map management
self.map_resolution = 0.1 # meters
self.max_map_size = 100.0 # meters
self.get_logger().info('VSLAM Map initialized')
def add_landmark(self, position, descriptor=None):
"""Add a new landmark to the map"""
landmark_id = self.landmark_id_counter
self.landmark_id_counter += 1
# Store landmark with its properties
self.landmarks[landmark_id] = {
'position': np.array(position),
'descriptor': descriptor,
'observations': 1,
'first_observed': self.get_clock().now().nanoseconds
}
# Publish updated map
self.publish_map()
return landmark_id
def update_landmark(self, landmark_id, new_position, weight=1.0):
"""Update an existing landmark position"""
if landmark_id in self.landmarks:
# Weighted average update
old_pos = self.landmarks[landmark_id]['position']
self.landmarks[landmark_id]['position'] = (
(self.landmarks[landmark_id]['position'] * self.landmarks[landmark_id]['observations'] +
new_position * weight) /
(self.landmarks[landmark_id]['observations'] + weight)
)
self.landmarks[landmark_id]['observations'] += 1
# Publish updated map
self.publish_map()
def publish_map(self):
"""Publish the current map as a point cloud"""
# Create PointCloud2 message
header = Header()
header.stamp = self.get_clock().now().to_msg()
header.frame_id = 'map'
# Prepare point cloud data
points = []
for landmark in self.landmarks.values():
pos = landmark['position']
points.append([pos[0], pos[1], pos[2]])
# Create the PointCloud2 message structure
# This is a simplified version - in a real implementation,
# you would use the sensor_msgs_py package or similar
if points:
# Create point cloud in the format needed
cloud_points = np.array(points, dtype=np.float32)
# In a full implementation, we would properly format
# the PointCloud2 message here
pass
def get_nearby_landmarks(self, position, radius=2.0):
"""Get landmarks within a specified radius of the given position"""
nearby = []
for landmark_id, landmark in self.landmarks.items():
dist = np.linalg.norm(landmark['position'] - position)
if dist <= radius:
nearby.append((landmark_id, landmark))
return nearby
def cleanup_old_landmarks(self):
"""Remove landmarks that haven't been observed recently"""
current_time = self.get_clock().now().nanoseconds
threshold_time = 60e9 # 60 seconds in nanoseconds
# Remove landmarks not observed for more than threshold_time
for landmark_id, landmark in list(self.landmarks.items()):
if current_time - landmark['first_observed'] > threshold_time:
# Additional check: if observed less than 3 times, remove it
if landmark['observations'] < 3:
del self.landmarks[landmark_id]
Safety-First VSLAM Implementation
For humanoid robotics applications, VSLAM must implement safety considerations:
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from std_msgs.msg import Bool
from sensor_msgs.msg import Image
from geometry_msgs.msg import Twist
class SafeVSLAM(Node):
def __init__(self):
super().__init__('safe_vslam')
# Subscribe to camera and safety topics
self.image_sub = self.create_subscription(
Image,
'/camera/image',
self.image_callback,
10
)
# Publisher for movement commands with safety
self.cmd_pub = self.create_publisher(Twist, '/cmd_vel', 10)
self.emergency_stop_pub = self.create_publisher(Bool, '/emergency_stop', 10)
# Initialize components
self.feature_tracker = FeatureTracker()
self.pose_estimator = PoseEstimator()
self.map_builder = VSLAMMap()
# Safety parameters
self.position_uncertainty_threshold = 0.5 # meters
self.min_feature_count = 10
self.max_velocity = 0.3 # m/s
self.get_logger().info('Safe VSLAM system initialized')
def image_callback(self, msg):
"""Process image with safety considerations"""
# Extract features
features = self.extract_features(msg)
# Check if we have enough features for reliable localization
if len(features) < self.min_feature_count:
self.get_logger().warn(f'Not enough features for reliable localization: {len(features)}')
# Consider whether to stop or reduce speed
if len(features) < self.min_feature_count / 2:
self.slow_down_or_stop()
# Perform pose estimation
if self.pose_estimator.prev_features is not None:
# Estimate pose change
relative_transform = self.pose_estimator.estimate_pose(
self.pose_estimator.prev_features, features
)
# Check if pose uncertainty is too high
uncertainty = self.estimate_pose_uncertainty(relative_transform)
if uncertainty > self.position_uncertainty_threshold:
self.get_logger().warn(f'High pose uncertainty: {uncertainty:.2f}m')
self.slow_down_or_stop()
else:
# Update pose normally
self.pose_estimator.update_pose(relative_transform)
# Update feature tracker
self.feature_tracker.update_features(msg, features)
def estimate_pose_uncertainty(self, transform):
"""Estimate the uncertainty of the pose estimate"""
# Calculate uncertainty based on number of features and their distribution
# This is a simplified version - real implementation would be more complex
rotation = transform[:3, :3]
translation = transform[:3, 3]
# Higher uncertainty if transformation has high rotation or translation
# relative to what we expect for the robot's movement
rotation_angle = np.arccos(np.clip((np.trace(rotation) - 1) / 2, -1, 1))
translation_norm = np.linalg.norm(translation)
# Combine rotation and translation uncertainties
uncertainty = rotation_angle + translation_norm
return uncertainty
def slow_down_or_stop(self):
"""Slow down or stop robot movement for safety"""
stop_cmd = Twist()
self.cmd_pub.publish(stop_cmd)
self.get_logger().warn('Robot movement stopped for safety')
AI Transparency in VSLAM
All VSLAM decisions must be logged for transparency and auditability:
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
import json
import datetime
class VSLAMTransparencyLogger(Node):
def __init__(self):
super().__init__('vslam_transparency_logger')
# Publisher for transparency logs
self.log_pub = self.create_publisher(String, '/vslam_transparency_log', 10)
# Initialize log storage
self.decision_log = []
self.get_logger().info('VSLAM Transparency Logger initialized')
def log_tracking_decision(self, frame_id, feature_count, tracking_quality):
"""Log a tracking decision for transparency"""
timestamp = self.get_clock().now().to_msg()
log_entry = {
'timestamp': timestamp.sec + timestamp.nanosec / 1e9,
'decision_type': 'feature_tracking',
'frame_id': frame_id,
'feature_count': feature_count,
'tracking_quality': tracking_quality,
'reasoning': f'Tracked {feature_count} features with quality {tracking_quality}',
'node_id': self.get_name()
}
# Store in memory
self.decision_log.append(log_entry)
# Publish to topic for external logging
log_msg = String()
log_msg.data = json.dumps(log_entry)
self.log_pub.publish(log_msg)
self.get_logger().info(f'Logged tracking decision: {feature_count} features, quality {tracking_quality}')
def log_pose_decision(self, position, rotation, uncertainty):
"""Log a pose estimation decision"""
timestamp = self.get_clock().now().to_msg()
log_entry = {
'timestamp': timestamp.sec + timestamp.nanosec / 1e9,
'decision_type': 'pose_estimation',
'position': position.tolist() if isinstance(position, np.ndarray) else position,
'rotation': rotation.tolist() if isinstance(rotation, np.ndarray) else rotation,
'uncertainty': uncertainty,
'reasoning': f'Estimated pose with uncertainty {uncertainty:.3f}',
'node_id': self.get_name()
}
# Store in memory
self.decision_log.append(log_entry)
# Publish to topic for external logging
log_msg = String()
log_msg.data = json.dumps(log_entry)
self.log_pub.publish(log_msg)
self.get_logger().info(f'Logged pose decision with uncertainty {uncertainty:.3f}')
VSLAM systems for humanoid robots must balance accuracy, computational efficiency, and safety. The implementation should include proper error handling, uncertainty estimation, and fallback strategies to ensure safe robot operation in all conditions. Transparency in algorithm decisions is essential for debugging and validation purposes.