Optimal Clustering of Point Cloud by 2D-LiDAR Using Kalman Filter

Abstract

Light detection and ranging (LiDAR) has been the primary sensor for autonomous mobility and navigation system owing to its stability. Although multiple-channel LiDAR (3D-LiDAR) can obtain dense point clouds that provide optimal performance for several tasks, the application scope is limited by its high-cost. When employing single channel LiDAR (2D-LiDAR) as a low-cost alternative, the quantity and quality of the point cloud cause conventional methods to perform poorly in clustering and tracking tasks. In particular, when handling multiple pedestrian scenarios, the point cloud is not distinguished and clustering is unable to succeed. Hence, we propose an optimized clustering method combined with a Kalman filter (KF) for simultaneous clustering and tracking applicable to 2D-LiDAR.