Tracking Multiple
Moving Objects with a Mobile Robot
Proc. of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (CVPR-01)
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.