D. Schulz, W. Burgard, D. Fox, and A.B. Cremers
Tracking Multiple Moving Objects with a Mobile Robot
In Proc. of the IEEE Computer Society Conference on Computer Vision and
Pattern Recognition (CVPR),
2001.
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. In this
paper 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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Bibtex
@InProceedings{Schulz01Tracking,
author = {Schulz, D. and Burgard, W.
and Fox, D. and Cremers, A.B.},
title = {Tracking Multiple Moving Objects with a Mobile Robot},
booktitle = {Proc. of the IEEE Computer Society Conference on Computer Vision and
Pattern Recognition (CVPR)},
year = {2001}
}