Abstract
One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can determine the positions of the humans in its surrounding. We introduce sample-based joint probabilistic data association filters to track multiple moving objects with a mobile robot. Our technique uses the robot's sensors and a motion model of the objects being tracked. A Bayesian filtering technique is applied to adapt the tracking process to the number of objects in the sensor range of the robot. Our approach to tracking multiple moving objects has been implemented and tested on a real robot. We present experiments illustrating that our approach is able to robustly keep track of multiple persons even in situations in which people are temporarily occluded. The experiments furthermore show that the approach outperforms other techniques developed so far.
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