sensor-fusion

# Sensor Fusion Skill ## Purpose Enable multi-sensor fusion algorithm development for autonomous driving perception including object detection, tracking, and environmental modeling. ## Capabilities - Camera, radar, lidar data preprocessing - Object detection fusion algorithms - Tracking filter implementation (Kalman, EKF, UKF) - Association algorithms (Hungarian, GNN, JPDA) - Occupancy grid fusion - Confidence estimation and sensor weighting - Time synchronization handling - Ground truth comparison and metrics ## Usage Guidelines - Preprocess sensor data for consistent coordinate frames - Select appropriate tracking filters based on object dynamics - Implement robust association for multi-target scenarios - Fuse sensor confidence for reliable perception - Handle time delays and synchronization issues - Validate fusion against ground truth data ## Dependencies - ROS/ROS2 - TensorFlow - PyTorch - NVIDIA DriveWorks ## Process Integration - ADA-001: Perception System Development - ADA-002: Path Planning and Motion Control - ADA-003: ADAS Feature Development - ADA-004: Simulation and Virtual Validation