Detection and tracking of dynamic objects using 3D laser range sensor on a mobile platform

In this paper we present an algorithm for detection, extraction and tracking of moving objects using a 3D laser range sensor. First, ground extraction is performed using random sample consensus for model parameter estimation. Afterwards, to downsample the point cloud, a voxel grid filtering is executed and octree data structure is used. This data structure enables an efficient detection of differences between two consecutive point clouds, based on which clustering of dynamic parts of the cloud is performed. The obtained clusters are then expanded over the set of static voxels in order to cover entire objects. In order to account for ego-motion an iterative closest point registration technique with an initial transformation guess obtained by odometry of the platform is used. As the final step, we present a tracking algorithm based on joint probabilistic data association (JPDA) filter with variable process and measurement noise taking into account velocity and position of the tracked objects. However, JPDA filter assumes a constant and known number of objects in the scene, and therefore we use track management based on entropy. Experiments are performed using a setup consisting of a Velodyne HDL-32E mounted on top of a mobile platform in order to verify the developed algorithms.