# Robot Perception > Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines. - Skill: `majiayu000/robot-perception` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds add majiayu000/robot-perception` - Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/robot-perception/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: majiayu000 (https://skillmd.com/u/majiayu000) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/majiayu000/robot-perception --- # Robot Perception Skill ## When to Use This Skill - Setting up and configuring camera, LiDAR, or depth sensors - Building RGB, depth, or point cloud processing pipelines - Calibrating cameras (intrinsic, extrinsic, hand-eye) - Implementing object detection, segmentation, or tracking for robots - Fusing data from multiple sensor modalities - Streaming sensor data with proper threading and buffering - Synchronizing multi-sensor rigs - Deploying perception models on robot hardware (GPU, edge) - Debugging perception failures (latency, dropped frames, misalignment) ## Sensor Landscape ### Sensor Types and Characteristics ``` Sensor Type Output Range Rate Best For ───────────────────────────────────────────────────────────────────────── RGB Camera (H,W,3) uint8 ∞ 30-120Hz Object detection, tracking, visual servoing Stereo Camera (H,W,3)+(H,W,3) 0.3-20m 30-90Hz Dense depth from passive stereo Structured Light (H,W) float + RGB 0.2-10m 30Hz Indoor manipulation, short range ToF Depth (H,W) float + RGB 0.1-10m 30Hz Indoor, medium range LiDAR (spinning) (N,3) or (N,4) 0.5-200m 10-20Hz Outdoor navigation, mapping LiDAR (solid-st.) (N,3) 0.5-200m 10-30Hz Automotive, outdoor IMU (6,) or (9,) N/A 200-1kHz Orientation, motion estimation Force/Torque (6,) float N/A 1kHz+ Contact detection, force control Tactile (H,W) or (N,3) Contact 30-100Hz Grasp quality, texture Event Camera Events (x,y,t,p) ∞ μs High-speed tracking, HDR scenes ``` ### Common Sensor Hardware ``` Device Type SDK/Driver ROS2 Package ────────────────────────────────────────────────────────────────────────── Intel RealSense Structured Light pyrealsense2 realsense2_camera Stereolabs ZED Stereo + IMU pyzed zed_wrapper Luxonis OAK-D Stereo + Neural depthai depthai_ros FLIR/Basler Industrial RGB PySpin/pypylon spinnaker_camera_driver Velodyne Spinning LiDAR velodyne_driver velodyne Ouster Spinning LiDAR ouster-sdk ros2_ouster Livox Solid-state LiDAR livox_sdk livox_ros2_driver USB Webcam RGB OpenCV VideoCapture usb_cam / v4l2_camera ``` ## Camera Models and Calibration ### Pinhole Camera Model ``` 3D World Point (X, Y, Z) | [R | t] — Extrinsic (world → camera) | Camera Point (Xc, Yc, Zc) | K — Intrinsic (camera → pixel) | Pixel (u, v) K = [ fx 0 cx ] fx, fy = focal lengths (pixels) [ 0 fy cy ] cx, cy = principal point [ 0 0 1 ] Projection: [u, v, 1]^T = K @ [R | t] @ [X, Y, Z, 1]^T ``` ### Intrinsic Calibration ```python import cv2 import numpy as np from pathlib import Path class IntrinsicCalibrator: """Camera intrinsic calibration using checkerboard pattern""" def __init__(self, board_size=(9, 6), square_size_m=0.025): self.board_size = board_size self.square_size = square_size_m # Prepare object points (3D coordinates of checkerboard corners) self.objp = np.zeros((board_size[0] * board_size[1], 3), np.float32) self.objp[:, :2] = np.mgrid[ 0:board_size[0], 0:board_size[1] ].T.reshape(-1, 2) * square_size_m def collect_calibration_images(self, camera, num_images=30, min_coverage=0.6): """Collect calibration images with good spatial coverage. IMPORTANT: Move the board to cover all regions of the image, including corners and edges. Tilt the board at various angles. Bad coverage = bad calibration, especially at image edges. """ obj_points = [] img_points = [] coverage_map = np.zeros((4, 4), dtype=int) # Track board positions while len(obj_points) < num_images: frame = camera.capture() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) found, corners = cv2.findChessboardCorners( gray, self.board_size, cv2.CALIB_CB_ADAPTIVE_THRESH | cv2.CALIB_CB_NORMALIZE_IMAGE | cv2.CALIB_CB_FAST_CHECK ) if found: # Sub-pixel refinement — critical for accuracy criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) corners = cv2.cornerSubPix( gray, corners, (11, 11), (-1, -1), criteria) # Track coverage center = corners.mean(axis=0).flatten() grid_x = int(center[0] / gray.shape[1] * 4) grid_y = int(center[1] / gray.shape[0] * 4) grid_x = min(grid_x, 3) grid_y = min(grid_y, 3) coverage_map[grid_y, grid_x] += 1 obj_points.append(self.objp) img_points.append(corners) coverage = (coverage_map > 0).sum() / coverage_map.size if coverage < min_coverage: print(f"WARNING: Only {coverage:.0%} coverage. " f"Move board to uncovered regions.") return obj_points, img_points, gray.shape[::-1] def calibrate(self, obj_points, img_points, image_size): """Run calibration and return camera matrix + distortion coeffs""" ret, K, dist, rvecs, tvecs = cv2.calibrateCamera( obj_points, img_points, image_size, None, None) if ret > 1.0: print(f"WARNING: High reprojection error ({ret:.3f} px). " f"Check image quality and board detection.") # Compute per-image reprojection errors errors = [] for i in range(len(obj_points)): projected, _ = cv2.projectPoints( obj_points[i], rvecs[i], tvecs[i], K, dist) err = cv2.norm(img_points[i], projected, cv2.NORM_L2) err /= len(projected) errors.append(err) print(f"Calibration complete:") print(f" RMS reprojection error: {ret:.4f} px") print(f" Per-image errors: mean={np.mean(errors):.4f}, " f"max={np.max(errors):.4f}") print(f" Focal length: fx={K[0,0]:.1f}, fy={K[1,1]:.1f}") print(f" Principal point: cx={K[0,2]:.1f}, cy={K[1,2]:.1f}") return CalibrationResult( camera_matrix=K, dist_coeffs=dist, rms_error=ret, image_size=image_size) def save(self, result, path): """Save calibration to YAML (OpenCV-compatible format)""" fs = cv2.FileStorage(str(path), cv2.FILE_STORAGE_WRITE) fs.write("camera_matrix", result.camera_matrix) fs.write("dist_coeffs", result.dist_coeffs) fs.write("image_width", result.image_size[0]) fs.write("image_height", result.image_size[1]) fs.write("rms_error", result.rms_error) fs.release() @staticmethod def load(path): """Load calibration from YAML""" fs = cv2.FileStorage(str(path), cv2.FILE_STORAGE_READ) K = fs.getNode("camera_matrix").mat() dist = fs.getNode("dist_coeffs").mat() w = int(fs.getNode("image_width").real()) h = int(fs.getNode("image_height").real()) fs.release() return CalibrationResult( camera_matrix=K, dist_coeffs=dist, image_size=(w, h), rms_error=0.0) ``` ### Extrinsic Calibration (Camera-to-Camera, Camera-to-LiDAR) ```python class ExtrinsicCalibrator: """Compute transform between two sensors using shared targets""" def calibrate_stereo(self, calib_left, calib_right, obj_points, img_points_left, img_points_right, image_size): """Stereo calibration: find relative pose between two cameras""" ret, K1, d1, K2, d2, R, T, E, F = cv2.stereoCalibrate( obj_points, img_points_left, img_points_right, calib_left.camera_matrix, calib_left.dist_coeffs, calib_right.camera_matrix, calib_right.dist_coeffs, image_size, flags=cv2.CALIB_FIX_INTRINSIC # Use pre-calibrated intrinsics ) print(f"Stereo calibration RMS: {ret:.4f} px") print(f"Baseline: {np.linalg.norm(T):.4f} m") return StereoCalibration(R=R, T=T, E=E, F=F, rms_error=ret) def calibrate_camera_to_lidar(self, camera_points_2d, lidar_points_3d, K, dist): """Find camera-to-LiDAR transform using corresponding points. Use a calibration target visible to both sensors (e.g., checkerboard with reflective tape corners). """ # PnP: find pose of 3D points relative to camera success, rvec, tvec = cv2.solvePnP( lidar_points_3d, camera_points_2d, K, dist, flags=cv2.SOLVEPNP_ITERATIVE ) if not success: raise CalibrationError("PnP failed — check point correspondences") R, _ = cv2.Rodrigues(rvec) T_camera_lidar = np.eye(4) T_camera_lidar[:3, :3] = R T_camera_lidar[:3, 3] = tvec.flatten() # Verify by reprojecting projected, _ = cv2.projectPoints( lidar_points_3d, rvec, tvec, K, dist) error = np.mean(np.linalg.norm( camera_points_2d - projected.reshape(-1, 2), axis=1)) print(f"Camera-LiDAR reprojection error: {error:.2f} px") return T_camera_lidar ``` ### Hand-Eye Calibration (Camera-to-Robot) ```python class HandEyeCalibrator: """Solve AX = XB for camera mounted on robot end-effector (eye-in-hand) or camera mounted on a fixed base (eye-to-hand). Requires moving the robot to multiple poses while observing a fixed calibration target. """ def __init__(self, K, dist, board_size=(9, 6), square_size=0.025): self.K = K self.dist = dist self.board_size = board_size self.square_size = square_size self.objp = np.zeros((board_size[0] * board_size[1], 3), np.float32) self.objp[:, :2] = np.mgrid[ 0:board_size[0], 0:board_size[1] ].T.reshape(-1, 2) * square_size def collect_poses(self, camera, robot, num_poses=20): """Collect camera-target and robot poses at multiple configurations. IMPORTANT: Move to diverse robot orientations. At least 3 different rotation axes. Pure translations are NOT sufficient. """ R_gripper2base = [] t_gripper2base = [] R_target2cam = [] t_target2cam = [] for i in range(num_poses): input(f"Move robot to pose {i+1}/{num_poses}, press Enter...") # Get robot end-effector pose ee_pose = robot.get_ee_pose() # 4x4 homogeneous matrix R_gripper2base.append(ee_pose[:3, :3]) t_gripper2base.append(ee_pose[:3, 3]) # Detect calibration target in camera frame = camera.capture() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) found, corners = cv2.findChessboardCorners( gray, self.board_size, None) if not found: print(f" Board not detected at pose {i+1}, skip.") continue corners = cv2.cornerSubPix( gray, corners, (11, 11), (-1, -1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)) ret, rvec, tvec = cv2.solvePnP( self.objp, corners, self.K, self.dist) R, _ = cv2.Rodrigues(rvec) R_target2cam.append(R) t_target2cam.append(tvec.flatten()) return (R_gripper2base, t_gripper2base, R_target2cam, t_target2cam) def calibrate_eye_in_hand(self, R_g2b, t_g2b, R_t2c, t_t2c): """Eye-in-hand: camera mounted on end-effector. Solves for T_camera_to_gripper.""" R, t = cv2.calibrateHandEye( R_g2b, t_g2b, R_t2c, t_t2c, method=cv2.CALIB_HAND_EYE_TSAI # Also: PARK, HORAUD, DANIILIDIS ) T_cam2gripper = np.eye(4) T_cam2gripper[:3, :3] = R T_cam2gripper[:3, 3] = t.flatten() return T_cam2gripper def calibrate_eye_to_hand(self, R_g2b, t_g2b, R_t2c, t_t2c): """Eye-to-hand: camera fixed in workspace. Solves for T_camera_to_base.""" # Invert robot poses (base-to-gripper → gripper-to-base) R_b2g = [R.T for R in R_g2b] t_b2g = [-R.T @ t for R, t in zip(R_g2b, t_g2b)] R, t = cv2.calibrateHandEye( R_b2g, t_b2g, R_t2c, t_t2c, method=cv2.CALIB_HAND_EYE_TSAI ) T_cam2base = np.eye(4) T_cam2base[:3, :3] = R T_cam2base[:3, 3] = t.flatten() return T_cam2base def verify_calibration(self, T_cam2ee, robot, camera, target_points_3d): """Verify by projecting a known 3D point through the full chain. Error should be < 5mm for manipulation tasks.""" ee_pose = robot.get_ee_pose() T_cam2base = ee_pose @ T_cam2ee # Project world point to camera point_in_cam = np.linalg.inv(T_cam2base) @ np.append( target_points_3d[0], 1.0) projected, _ = cv2.projectPoints( point_in_cam[:3].reshape(1, 3), np.zeros(3), np.zeros(3), self.K, self.dist) print(f"Verification projection: {projected.flatten()}") return projected.flatten() ``` ### Calibration Quality Checklist ``` ✅ Intrinsic calibration: - RMS reprojection error < 0.5 px (good), < 0.3 px (excellent) - ≥ 20 images with board covering full image area (corners + edges) - Board tilted at multiple angles (not just fronto-parallel) - Fixed focus / fixed zoom during calibration AND operation ✅ Extrinsic calibration (stereo / camera-LiDAR): - RMS < 1.0 px for stereo - Reprojection error < 3 px for camera-LiDAR - Verified with independent measurement (ruler / known distance) ✅ Hand-eye calibration: - ≥ 15 poses with diverse orientations (≥ 3 rotation axes) - Verification error < 5mm for manipulation, < 10mm for navigation - Robot repeatability accounted for (UR5 ≈ ±0.03mm, low-cost ≈ ±1mm) ⚠️ Recalibrate when: - Camera is physically bumped or remounted - Lens focus or zoom is changed - Temperature changes significantly (thermal expansion shifts intrinsics) - Robot is re-homed or joints are recalibrated ``` ## Sensor Streaming Best Practices ### Camera Streaming Architecture ```python import threading import time from collections import deque from dataclasses import dataclass, field from typing import Optional import numpy as np @dataclass class StampedFrame: """Frame with capture timestamp for synchronization""" data: np.ndarray timestamp: float # time.monotonic() at capture sequence: int # Frame counter sensor_id: str class CameraStream: """Thread-safe camera streaming with bounded buffer. Design principles: 1. Capture runs in a dedicated thread at sensor rate 2. Processing never blocks capture 3. Always use the LATEST frame (drop old ones for real-time) 4. Timestamp at capture, not at processing time """ def __init__(self, camera, buffer_size=2, name="camera"): self.camera = camera self.name = name self._buffer = deque(maxlen=buffer_size) self._lock = threading.Lock() self._new_frame = threading.Event() self._running = False self._sequence = 0 self._thread = None # Diagnostics self._capture_times = deque(maxlen=100) self._drop_count = 0 def start(self): """Start capture thread""" self._running = True self._thread = threading.Thread( target=self._capture_loop, daemon=True, name=f"{self.name}_capture") self._thread.start() def stop(self): """Stop capture thread""" self._running = False if self._thread: self._thread.join(timeout=2.0) def _capture_loop(self): while self._running: t_start = time.monotonic() try: raw = self.camera.capture() timestamp = time.monotonic() # Timestamp AFTER capture frame = StampedFrame( data=raw, timestamp=timestamp, sequence=self._sequence, sensor_id=self.name ) self._sequence += 1 with self._lock: if len(self._buffer) == self._buffer.maxlen: self._drop_count += 1 self._buffer.append(frame) self._new_frame.set() self._capture_times.append(time.monotonic() - t_start) except Exception as e: print(f"[{self.name}] Capture error: {e}") time.sleep(0.01) # Back off on error def get_latest(self) -> Optional[StampedFrame]: """Get most recent frame (non-blocking). Returns None if empty.""" with self._lock: if self._buffer: return self._buffer[-1] return None def wait_for_frame(self, timeout=1.0) -> Optional[StampedFrame]: """Block until a new frame arrives""" self._new_frame.clear() if self._new_frame.wait(timeout=timeout): return self.get_latest() return None def get_diagnostics(self) -> dict: """Streaming health metrics""" if self._capture_times: times = list(self._capture_times) fps = 1.0 / np.mean(times) if np.mean(times) > 0 else 0 else: fps = 0 return { "sensor": self.name, "fps": round(fps, 1), "frames_captured": self._sequence, "frames_dropped": self._drop_count, "buffer_size": len(self._buffer), "avg_capture_ms": round(np.mean(times) * 1000, 1) if self._capture_times else 0, } ``` ### Multi-Sensor Synchronization ```python class SyncedMultiSensor: """Synchronize frames from multiple sensors by timestamp. Uses nearest-neighbor matching within a time tolerance. For hardware-synced sensors, use hardware trigger instead. """ def __init__(self, sensors: dict, max_time_diff_ms=33): """ Args: sensors: {"rgb": CameraStream, "depth": CameraStream, ...} max_time_diff_ms: Maximum allowed time difference between synced frames. Default 33ms (1 frame at 30Hz). """ self.sensors = sensors self.max_dt = max_time_diff_ms / 1000.0 self._synced_callback = None def start(self): for s in self.sensors.values(): s.start() def stop(self): for s in self.sensors.values(): s.stop() def get_synced(self) -> Optional[dict]: """Get time-synchronized frames from all sensors. Returns None if any sensor is missing or too far out of sync.""" frames = {} for name, stream in self.sensors.items(): frame = stream.get_latest() if frame is None: return None frames[name] = frame # Check time alignment against the first sensor ref_time = list(frames.values())[0].timestamp for name, frame in frames.items(): dt = abs(frame.timestamp - ref_time) if dt > self.max_dt: return None # Out of sync return frames def get_synced_interpolated(self) -> Optional[dict]: """For sensors at different rates, interpolate to common timestamp. Useful for IMU + camera fusion.""" # Get latest from each sensor frames = {} for name, stream in self.sensors.items(): frame = stream.get_latest() if frame is None: return None frames[name] = frame # Use the SLOWEST sensor's timestamp as reference ref_time = min(f.timestamp for f in frames.values()) result = {} for name, frame in frames.items(): dt = frame.timestamp - ref_time if abs(dt) <= self.max_dt: result[name] = frame # For high-rate sensors (IMU), could interpolate here return result if len(result) == len(self.sensors) else None ``` ### Hardware-Triggered Synchronization ```python class HardwareSyncConfig: """Configure hardware trigger for multi-camera synchronization. ALWAYS prefer hardware sync over software sync for: - Stereo depth computation - Multi-camera 3D reconstruction - Fast-moving scenes Common trigger methods: - GPIO pulse from microcontroller (Arduino, ESP32) - Camera's own sync output → other cameras' trigger input - PTP (Precision Time Protocol) for GigE cameras """ @staticmethod def setup_realsense_sync(master_serial, slave_serials): """Configure RealSense hardware sync (Inter-Cam sync mode)""" import pyrealsense2 as rs # Master camera: generates sync signal master = rs.pipeline() master_cfg = rs.config() master_cfg.enable_device(master_serial) master_sensor = master.start(master_cfg) # Set master to mode 1 (master) depth_sensor = master_sensor.get_device().first_depth_sensor() depth_sensor.set_option(rs.option.inter_cam_sync_mode, 1) # Slave cameras: receive sync signal slaves = [] for serial in slave_serials: pipe = rs.pipeline() cfg = rs.config() cfg.enable_device(serial) profile = pipe.start(cfg) sensor = profile.get_device().first_depth_sensor() sensor.set_option(rs.option.inter_cam_sync_mode, 2) # Slave slaves.append(pipe) return master, slaves @staticmethod def setup_ptp_sync(camera_ips): """Enable PTP synchronization for GigE Vision cameras. Requires PTP-capable network switch. Achieves < 1μs sync accuracy. """ # Example with Basler/pylon cameras # import pypylon.pylon as py # # for ip in camera_ips: # cam = py.InstantCamera(py.TlFactory.GetInstance() # .CreateDevice(py.CDeviceInfo().SetIpAddress(ip))) # cam.Open() # cam.GevIEEE1588.Value = True # Enable PTP # cam.Close() pass ``` ### Streaming Anti-Patterns ```python # ❌ BAD: Capture and process in same thread — limits to slowest operation def bad_pipeline(): while True: frame = camera.capture() # 5ms detections = model.detect(frame) # 100ms # Effective rate: ~9 FPS regardless of camera's 30 FPS # ✅ GOOD: Decouple capture from processing def good_pipeline(): stream = CameraStream(camera) stream.start() while True: frame = stream.get_latest() # Always latest, non-blocking if frame: detections = model.detect(frame.data) # 100ms # Camera still capturing at 30 FPS in background # Processing at ~10 FPS but always on freshest frame # ❌ BAD: Unbounded buffer — memory grows until OOM buffer = [] # Will grow forever if consumer is slower than producer! # ✅ GOOD: Bounded buffer with drop policy buffer = deque(maxlen=2) # Keep latest 2, drop old automatically # ❌ BAD: Sleeping for frame timing time.sleep(1.0 / 30) # Drift accumulates, doesn't account for jitter # ✅ GOOD: Event-driven or spin with wall clock frame = stream.wait_for_frame(timeout=0.1) # ❌ BAD: Timestamp at processing time def process_callback(raw_data): timestamp = time.time() # WRONG: includes queue delay + scheduling frame = decode(raw_data) publish(frame, timestamp) # ✅ GOOD: Timestamp at capture time def capture_thread(): raw_data = sensor.read() timestamp = time.monotonic() # RIGHT: timestamp at source queue.put((raw_data, timestamp)) ``` ## RGB Processing Pipelines ### Image Undistortion ```python class ImageUndistorter: """Pre-compute undistortion maps for fast runtime correction. ALWAYS undistort before any geometric computation (pose estimation, 3D reconstruction, visual servoing). """ def __init__(self, K, dist, image_size, alpha=0.0): """ Args: alpha: 0.0 = crop all black pixels (default, recommended) 1.0 = keep all pixels (shows black borders) """ self.new_K, self.roi = cv2.getOptimalNewCameraMatrix( K, dist, image_size, alpha, image_size) # Pre-compute maps (do this ONCE, not per frame) self.map1, self.map2 = cv2.initUndistortRectifyMap( K, dist, None, self.new_K, image_size, cv2.CV_16SC2) def undistort(self, image): """Apply undistortion using pre-computed maps (fast)""" return cv2.remap(image, self.map1, self.map2, interpolation=cv2.INTER_LINEAR) def undistort_points(self, points_2d): """Undistort 2D point coordinates""" return cv2.undistortPoints( points_2d.reshape(-1, 1, 2).astype(np.float64), self.K, self.dist, P=self.new_K ).reshape(-1, 2) ``` ### Object Detection Integration ```python class RobotObjectDetector: """Object detection for robotic manipulation. Wraps a detection model with robotics-specific post-processing: - Workspace filtering (ignore detections outside robot reach) - Stability tracking (reject flickering detections) - 3D pose estimation from 2D detections + depth """ def __init__(self, model, K, workspace_bounds=None, min_confidence=0.5): self.model = model self.K = K self.workspace_bounds = workspace_bounds self.min_confidence = min_confidence self.tracker = DetectionTracker(max_age=5, min_hits=3) def detect(self, rgb, depth=None) -> list: """Run detection with robotics post-processing""" # Raw detection raw_dets = self.model(rgb) # Filter by confidence dets = [d for d in raw_dets if d.confidence >= self.min_confidence] # Estimate 3D position if depth is available if depth is not None: for det in dets: det.position_3d = self._backproject(det.center, depth) # Filter by workspace if self.workspace_bounds and det.position_3d is not None: if not self.workspace_bounds.contains(det.position_3d): det.in_workspace = False continue det.in_workspace = True # Track across frames for stability tracked = self.tracker.update(dets) return tracked def _backproject(self, pixel, depth_image): """Convert 2D pixel + depth to 3D point in camera frame. This is the INVERSE of projection: X = (u - cx) * Z / fx Y = (v - cy) * Z / fy Z = depth """ u, v = int(pixel[0]), int(pixel[1]) # Sample depth with a small window (more robust than single pixel) h, w = depth_image.shape[:2] u = np.clip(u, 2, w - 3) v = np.clip(v, 2, h - 3) patch = depth_image[v-2:v+3, u-2:u+3] # Use median to reject outliers (holes, edges) valid = patch[patch > 0] if len(valid) == 0: return None Z = np.median(valid) # Convert from depth sensor units (often mm) to meters if Z > 100: # Likely millimeters Z = Z / 1000.0 fx, fy = self.K[0, 0], self.K[1, 1] cx, cy = self.K[0, 2], self.K[1, 2] X = (u - cx) * Z / fx Y = (v - cy) * Z / fy return np.array([X, Y, Z]) ``` ### Fiducial Marker Detection (AprilTag / ArUco) ```python class FiducialDetector: """Detect and estimate pose of fiducial markers. Use cases: - Robot localization (known marker positions) - Object pose estimation (marker attached to object) - Calibration targets - Sim-to-real ground truth """ def __init__(self, K, dist, marker_size_m=0.05, dictionary=cv2.aruco.DICT_APRILTAG_36h11): self.K = K self.dist = dist self.marker_size = marker_size_m self.aruco_dict = cv2.aruco.getPredefinedDictionary(dictionary) self.params = cv2.aruco.DetectorParameters() self.detector = cv2.aruco.ArucoDetector(self.aruco_dict, self.params) def detect(self, image) -> list: """Detect markers and estimate their 6DOF poses""" gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) corners, ids, rejected = self.detector.detectMarkers(gray) results = [] if ids is not None: for i, marker_id in enumerate(ids.flatten()): # Estimate pose (rotation + translation) rvecs, tvecs, _ = cv2.aruco.estimatePoseSingleMarkers( corners[i:i+1], self.marker_size, self.K, self.dist) R, _ = cv2.Rodrigues(rvecs[0]) T = np.eye(4) T[:3, :3] = R T[:3, 3] = tvecs[0].flatten() results.append(MarkerDetection( marker_id=int(marker_id), corners=corners[i].reshape(4, 2), pose=T, distance=np.linalg.norm(tvecs[0]), )) return results def draw(self, image, detections): """Visualize detected markers with axes""" vis = image.copy() for det in detections: cv2.aruco.drawDetectedMarkers( vis, [det.corners.reshape(1, 4, 2)], np.array([[det.marker_id]])) rvec, _ = cv2.Rodrigues(det.pose[:3, :3]) cv2.drawFrameAxes( vis, self.K, self.dist, rvec, det.pose[:3, 3], self.marker_size * 0.5) return vis ``` ### Semantic Segmentation for Manipulation ```python class WorkspaceSegmenter: """Segment a robot's workspace into actionable regions. Common segmentation targets for manipulation: - Graspable objects vs background - Table / support surface - Robot body (for self-collision avoidance) - Free space vs obstacles """ def __init__(self, model, class_map): self.model = model self.class_map = class_map # {0: 'background', 1: 'table', 2: 'object', ...} def segment(self, rgb) -> SegmentationResult: """Run segmentation and extract per-class masks""" mask = self.model.predict(rgb) # (H, W) with class IDs return SegmentationResult( full_mask=mask, object_mask=(mask == self.class_map['object']), table_mask=(mask == self.class_map['table']), free_space_mask=(mask == self.class_map['free_space']), ) def get_grasp_candidates(self, rgb, depth, K): """Combine segmentation with depth to find graspable regions""" seg = self.segment(rgb) # Find connected components in object mask num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats( seg.object_mask.astype(np.uint8)) candidates = [] for i in range(1, num_labels): # Skip background (0) component_mask = (labels == i) area = stats[i, cv2.CC_STAT_AREA] if area < 100: # Too small, likely noise continue # Get median depth of this object obj_depths = depth[component_mask] valid_depths = obj_depths[obj_depths > 0] if len(valid_depths) == 0: continue center_px = centroids[i] median_depth = np.median(valid_depths) # Backproject center to 3D fx, fy = K[0, 0], K[1, 1] cx, cy = K[0, 2], K[1, 2] X = (center_px[0] - cx) * median_depth / fx Y = (center_px[1] - cy) * median_depth / fy candidates.append(GraspCandidate( position_3d=np.array([X, Y, median_depth]), pixel_center=center_px, mask=component_mask, area_pixels=area, )) return candidates ``` ## Depth Processing ### Depth Map Filtering and Cleanup ```python class DepthProcessor: """Clean up raw depth maps for downstream use. Raw depth from sensors is noisy: holes, flying pixels at edges, multi-path interference, and range noise. Always filter before use. """ def __init__(self, depth_scale=0.001, min_depth_m=0.1, max_depth_m=3.0): self.depth_scale = depth_scale # Raw units to meters self.min_depth = min_depth_m self.max_depth = max_depth_m def process(self, raw_depth) -> np.ndarray: """Full depth cleanup pipeline""" depth = raw_depth.astype(np.float32) * self.depth_scale # 1. Range filtering depth[(depth < self.min_depth) | (depth > self.max_depth)] = 0 # 2. Edge filtering — remove flying pixels at depth discontinuities depth = self._remove_flying_pixels(depth) # 3. Hole filling — fill small holes via inpainting depth = self._fill_holes(depth, max_hole_size=10) # 4. Spatial smoothing — bilateral filter preserves edges depth = self._bilateral_filter(depth) return depth def _remove_flying_pixels(self, depth, threshold=0.05): """Remove pixels at depth discontinuities (flying pixels). These are artifacts where the sensor interpolates between foreground and background.""" dx = np.abs(np.diff(depth, axis=1, prepend=0)) dy = np.abs(np.diff(depth, axis=0, prepend=0)) gradient = np.sqrt(dx**2 + dy**2) depth[gradient > threshold] = 0 return depth def _fill_holes(self, depth, max_hole_size=10): """Fill small holes using inpainting. Large holes remain as 0.""" mask = (depth == 0).astype(np.uint8) # Only fill small holes kernel = np.ones((max_hole_size, max_hole_size), np.uint8) small_holes = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) small_holes = small_holes & mask if small_holes.any(): # Normalize depth to uint16 for inpainting d_norm = (depth * 1000).astype(np.uint16) filled = cv2.inpaint( d_norm, small_holes, max_hole_size, cv2.INPAINT_NS) depth = np.where(small_holes, filled.astype(np.float32) / 1000, depth) return depth def _bilateral_filter(self, depth, d=5, sigma_color=0.02, sigma_space=5): """Bilateral filter: smooths noise while preserving depth edges""" # Convert to uint16 for cv2 bilateral (doesn't support float) d_uint16 = (depth * 10000).astype(np.uint16) filtered = cv2.bilateralFilter(d_uint16, d, sigma_color * 10000, sigma_space) return filtered.astype(np.float32) / 10000 ``` ### Depth-to-Point Cloud Conversion ```python class DepthToPointCloud: """Convert depth images to organized and unorganized point clouds.""" def __init__(self, K, image_size): self.K = K # Pre-compute pixel coordinate grid (do once, reuse every frame) h, w = image_size[1], image_size[0] u_coords, v_coords = np.meshgrid(np.arange(w), np.arange(h)) self.u = u_coords.astype(np.float32) self.v = v_coords.astype(np.float32) self.fx, self.fy = K[0, 0], K[1, 1] self.cx, self.cy = K[0, 2], K[1, 2] def convert(self, depth, rgb=None) -> np.ndarray: """Convert depth image to point cloud. Returns: points: (N, 3) or (N, 6) if rgb provided [x, y, z, r, g, b] """ # Backproject all pixels in parallel (vectorized) Z = depth.astype(np.float32) X = (self.u - self.cx) * Z / self.fx Y = (self.v - self.cy) * Z / self.fy # Stack into (H, W, 3) organized point cloud points = np.stack([X, Y, Z], axis=-1) # Filter invalid points valid_mask = Z > 0 points_valid = points[valid_mask] if rgb is not None: colors = rgb[valid_mask].astype(np.float32) / 255.0 return np.hstack([points_valid, colors]) return points_valid def convert_organized(self, depth, rgb=None): """Return organized point cloud (H, W, 3) — preserves pixel layout. Invalid points are NaN.""" Z = depth.astype(np.float32) X = (self.u - self.cx) * Z / self.fx Y = (self.v - self.cy) * Z / self.fy organized = np.stack([X, Y, Z], axis=-1) organized[depth == 0] = np.nan return organized ``` ### RGBD Alignment ```python class RGBDAligner: """Align RGB and depth images from different sensors. Most RGBD sensors (RealSense, ZED) provide built-in alignment. Use this when combining separate RGB + depth cameras. """ def __init__(self, K_rgb, K_depth, T_depth_to_rgb, rgb_size, depth_size): self.K_rgb = K_rgb self.K_depth = K_depth self.T = T_depth_to_rgb # 4x4 transform: depth frame → rgb frame self.rgb_size = rgb_size self.depth_size = depth_size def align_depth_to_rgb(self, depth): """Reproject depth image into RGB camera's frame. For each pixel in depth image: 1. Backproject to 3D using depth intrinsics 2. Transform to RGB camera frame 3. Project to RGB pixel using RGB intrinsics """ h_d, w_d = depth.shape h_r, w_r = self.rgb_size[1], self.rgb_size[0] aligned = np.zeros((h_r, w_r), dtype=np.float32) # Backproject depth pixels to 3D v_d, u_d = np.mgrid[0:h_d, 0:w_d].astype(np.float32) Z = depth.astype(np.float32) valid = Z > 0 X = (u_d[valid] - self.K_depth[0, 2]) * Z[valid] / self.K_depth[0, 0] Y = (v_d[valid] - self.K_depth[1, 2]) * Z[valid] / self.K_depth[1, 1] pts_3d = np.stack([X, Y, Z[valid], np.ones_like(X)], axis=0) # Transform to RGB frame pts_rgb = self.T @ pts_3d # Project to RGB pixels u_r = (self.K_rgb[0, 0] * pts_rgb[0] / pts_rgb[2] + self.K_rgb[0, 2]).astype(int) v_r = (self.K_rgb[1, 1] * pts_rgb[1] / pts_rgb[2] + self.K_rgb[1, 2]).astype(int) # Fill aligned depth (z-buffer for occlusion handling) in_bounds = (u_r >= 0) & (u_r < w_r) & (v_r >= 0) & (v_r < h_r) for u, v, z in zip(u_r[in_bounds], v_r[in_bounds], pts_rgb[2][in_bounds]): if aligned[v, u] == 0 or z < aligned[v, u]: aligned[v, u] = z return aligned ``` ## Point Cloud Processing ### Filtering and Downsampling ```python import open3d as o3d class PointCloudProcessor: """Point cloud processing pipeline using Open3D. Pipeline order matters: 1. Crop to region of interest (reduces data volume first) 2. Remove statistical outliers 3. Downsample (voxel grid) 4. Estimate normals 5. Application-specific processing """ def preprocess(self, points, colors=None, workspace_bounds=None, voxel_size=0.005): """Standard preprocessing pipeline""" pcd = o3d.geometry.PointCloud() pcd.points = o3d.ut …(truncated)