People Tracking with Mobile Robots Using Sample-based Joint Probabilistic Data Association Filters  

Oleh:

Fox, Dieter ; Cremers, Armin B. ; Schulz, Dirk ; Burgard, Wolfram

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The International Journal of Robotics Research vol. 22 no. 2 (Feb. 2003), page 99-116.
Topik: multi-target tracking; data association; particle filters; people tracking; mobile robot perception
Fulltext: 99.pdf (1.11MB)

Isi artikel

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.

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