Short-Term Map Based Detection and Tracking of Moving Objects with 3D Laser on a Vehicle

Detection and tracking of moving objects is an essential problem in situational awareness context and hence crucial for many robotic applications. Here we propose a method for the detection of moving objects with a 3D laser range sensor and a variation of the method for tracking multiple detected objects. The detection procedure starts with the ground extraction using random sample consensus approach for model parameter estimation. The resulting point cloud is then downsampled using voxel grid approach and filtered using a radius outlier rejection method. Within the approach, we have utilized a procedure for building short-term maps of the environment by using the octree data structure. This data structure enables an efficient comparison of the current scan and the short-term local map, thus detecting dynamic parts of scene. The ego-motion of the mobile platform is compensated using the available odometry information, which is rather imperfect, and hence is refined using the iterative closest point registration technique. Furthermore, due to sensor characteristics, the iterative closest point is carried out in 2D between the short-term map and the current, where the non-ground filtered scans are projected onto 2D. The tracking task is based on the joint probabilistic data association filter and Kalman filtering with variable process and measurement noise which take into account velocity and position of the tracked objects. Since this data association approach assumes a constant and known number of objects, we have utilized a specific entropy based track management. The experiments performed using Velodyne HDL-32E laser sensor mounted on top of a mobile platform demonstrate the suitability and efficiency of the proposed method.