Volume 22 Issue 2 - Publication Date: 1 February 2003
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 Washingto, Department of computer Science and Engineering, Seattle, WA, USA and 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.
Multimedia Key
= Video = Data = Code = Image
SJPDAF in action: The animation demonstrates how the particle filters evolve over time while tracking four persons in a corridor. (2.2 MB)
Tracking Quality: To highlight the tracking quality, this clip shows a video of a tracking experiment and simultaniously for each frame a 3D graphics visualization constructed from the corresponding SJPDAF estimates. (26.4 MB)
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