How is lidar point cloud data processed for autonomous driving applications?

Point cloud processing pipeline: Ground plane segmentation separates road surface from objects using RANSAC or grid-based methods; clustering groups non-ground points into objects using algorithms like DBSCAN or Euclidean clustering; object classification identifies object type using 3D deep learning (PointNet, VoxelNet) or geometric features; bounding box fitting estimates object dimensions and orientation; tracking associates detections across frames. Additional processing includes: Registration (aligning scans for SLAM), feature extraction for localization, and freespace detection. Real-time requirements demand efficient algorithms and GPU/FPGA acceleration. Point cloud density varies with range, requiring handling of sparse data at distance.