This paper presents a method for moving-object tracking with in-vehicle 2D laser range sensor (LRS) in a cluttered environment. A sensing area of one LRS is limited in orientation, and hence the mobile robot is equipped with multi-LRSs for omnidirectional sensing. Since each LRS takes the laser image on its own local coordinate frame, the laser image is mapped onto a reference coordinate frame so that the object tracking can be achieved by cooperation of multi-LRSs. For mapping the coordinate frames of multi-LRSs are calibrated, that is, the relative positions and orientations of the multi-LRSs are estimated. The calibration is based on Kalman filter and chi-hypothesis testing. Moving-object tracking is achieved by two steps: detection and tracking. Each LRS finds moving objects from its own laser image via a heuristic rule and an occupancy grid based method. It tracks the moving objects via Kalman filter and the assignment algorithm based data association. When the moving objects exist in the overlapped sensing areas of the LRSs, these LRSs exchange the tracking data and fuse them in a decentralized manner. A rule based track management is embedded into the tracking system in order to enhance the tracking performance. The experimental result of three walking-people tracking in an indoor environment validates the proposed method.
Moving-Object Tracking with In-Vehicle Multi-Laser Range Sensors
