C. Kwok and D. Fox
Map-based Multiple Model Tracking of a Moving Object.
RoboCup Scientific Challenge (Best Paper) Award
Proceedings of RoboCup Symposium, 2004.
In this paper we propose an approach for tracking a moving target
using Rao-Blackwellised particle filters. Such filters represent
posteriors over the target location by a mixture of Kalman filters,
where each filter is conditioned on the discrete states of
a particle filter. The discrete states represent the non-linear
parts of the state estimation problem. In the context of target
tracking, these are the non-linear motion of the
observing platform and the different motion models for the target.
Using this representation, we show how to reason about physical
interactions between the observing platform and the tracked object,
as well as between the tracked object and the environment. The approach is
implemented on a four-legged AIBO robot and tested in the context of
ball tracking in the RoboCup domain.
Full paper [pdf]
(232 kb, 16 pages)
[To the RSE-lab]