Summary: Efficient People Tracking in Laser Range Data using a
Multi-Hypothesis Leg-Tracker with Adaptive Occlusion Probabilities
Kai O. Arras Slawomir Grzonka Matthias Luber Wolfram Burgard
Abstract-- We present an approach to laser-based people
tracking using a multi-hypothesis tracker that detects and
tracks legs separately with Kalman filters, constant velocity
motion models, and a multi-hypothesis data association strategy.
People are defined as high-level tracks consisting of two legs
that are found with little model knowledge. We extend the
data association so that it explicitly handles track occlusions in
addition to detections and deletions. Additionally, we adapt the
corresponding probabilities in a situation-dependent fashion so
as to reflect the fact that legs frequently occlude each other.
Experimental results carried out with a mobile robot illustrate
that our approach can robustly and efficiently track multiple
people even in situations of high levels of occlusion.
People tracking is a key technology for robots that oper-
ate in populated environments. Knowledge about presence,
position, and motion state of people will enable robots to