This article is part of the series Signal Processing Advances in Robots and Autonomy.

Open Access Research Article

Prioritized Multihypothesis Tracking by a Robot with Limited Sensing

Paul E Rybski* and Manuela M Veloso

Author Affiliations

School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

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EURASIP Journal on Advances in Signal Processing 2009, 2009:284525  doi:10.1155/2009/284525


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2009/1/284525


Received:1 August 2008
Accepted:1 December 2008
Published:11 January 2009

© 2009 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To act intelligently in dynamic environments, mobile robots must estimate object positions using information obtained from a variety of sources. We formally describe the problem of estimating the state of objects where a robot can only task its sensors to view one object at a time. We contribute an object tracking method that generates and maintains multiple hypotheses consisting of probabilistic state estimates that are generated by the individual information sources. These different hypotheses can be generated by the robot's own prediction model and by communicating robot team members. The multiple hypotheses are often spatially disjoint and cannot simultaneously be verified by the robot's limited sensors. Instead, the robot must decide towards which hypothesis its sensors should be tasked by evaluating each hypothesis on its likelihood of containing the object. Our contributed algorithm prioritizes the different hypotheses, according to rankings set by the expected uncertainty in the object's motion model, as well as the uncertainties in the sources of information used to track their positions. We describe the algorithm in detail and show extensive empirical results in simulation as well as experiments on actual robots that demonstrate the effectiveness of our approach.

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