Fast Object Detection and Tracking in Laser Data for Autonomous Driving

A fast algorithm to detecting and tracking multiple objects for an urban driving environment in multi-layer laser data is proposed in this paper. Since situational awareness is crucial for autonomous driving in complicate urban environments and challenging in 3D city perception. Object detection and tracking with cameras or laser has become a popular research topic. Compared with camera, multi-layer laser data is more suitable to estimate 3D model of object and predict motion. So 3D LiDAR is widely used in autonomous driving system. Model-based object tracking framework used in this paper relies on Kalman filter. We extract segmentation in each layer before clustering, which accelerates our detection step. Considering sub-segmentation and super-segmentation happens from time to time in object detection using sparse laser data, we associate the tracking history information with segmentation processing in a fast way. The proposed algorithm in this paper has been applied to the multi-layer laser set up on our autonomous driving vehicle. Experiments demonstrate the applicability and efficiency of our proposed algorithm under urban driving environment. On average, single frame processing takes 58 ms.