Laser-based Tracking of People and Vehicles by Multiple Mobile Robots

Abstract

This paper presents laser-based tracking of moving objects conducted by a group of mobile robots located near one another. Each robot finds moving objects such as people, cars, and bicycles in its own laser-scanned images using a binarized occupancy-grid-based method. It then sends laser measurements related to the detected moving objects to a central server. The central server estimates the pose and size of the moving objects via the Kalman filter based on received measurements; it then feeds that information back to the robots. Rule-based and global-nearest-neighbor-based data associations are applied for matching of tracked objects and laser measurements in multitarget environments. In this cooperative tracking method, the central server collects the laser measurements from all robots; hence, the robots can always track invisible or partially invisible objects. The experimental results for two robots in an outdoor environment validate our tracking method.