D. Schulz, D. Fox, and J. Hightower.
People Tracking with
Anonymous and ID-sensors Using Rao-Blackwellised Particle Filters.
Proc. of the International Joint Conference on
Artificial Intelligence (IJCAI-03).
Abstract Estimating the location of people using a network of sensors placed
throughout an environment is a fundamental challenge in smart
environments and ubiquitous computing. Id-sensors such as infrared
badges provide explicit object identity information but coarse
location information while anonymous sensors such as laser
range-finders provide accurate location information only. Tracking
using both sensor types simultaneously is an open research
challenge. We present a novel approach to tracking multiple objects
that combines the accuracy benefits of anonymous sensors and the
identification certainty of id-sensors. Rao-Blackwellised particle
filters are used to estimate object locations. Each particle
represents the association history between Kalman filtered object
tracks and observations. After using only anonymous sensors until
id estimates are certain enough, id assignments are sampled as well
resulting in a fully Rao-Blackwellised particle filter over both
object tracks and id assignments. Our approach was implemented and
tested successfully using data collected in an indoor environment.
Full paper [pdf]
(324 kb), 6 pages.
Note: This is a revised version that fixes a bug in the derivation of
the algorithm in Section 3.1. The algorithm itself is not affected by that.
[To the RSE-lab]