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The International Journal of Robotics Research
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People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters

Dirk Schulz

University of Bonn Computer Science Department Germany

Wolfram Burgard

University of Freiburg Department of Computer Science Germany

Dieter Fox

University of Washington Department of Computer Science & Engineering Seattle, WA, USA

Armin B. Cremers

University of Bonn Computer Science Department Germany

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 track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects. Our method applies Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot. The approach has been implemented and tested on a real robot using laser-range data. We present experiments illustrating that our algorithm is able to robustly keep track of multiple people. The experiments furthermore show that the approach outperforms other techniques developed so far.

Key Words: multi-target tracking • data association • particle filters • people tracking • mobile robot perception

The International Journal of Robotics Research, Vol. 22, No. 2, 99-116 (2003)
DOI: 10.1177/0278364903022002002
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