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  • Research Article
  • Open Access

Prioritized Multihypothesis Tracking by a Robot with Limited Sensing

EURASIP Journal on Advances in Signal Processing20092009:284525

  • Received: 1 August 2008
  • Accepted: 1 December 2008
  • Published:


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.


  • Team Member
  • Mobile Robot
  • State Estimate
  • Motion Model
  • Tracking Method

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Authors’ Affiliations

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


© P. E. Rybski and M. M. Veloso. 2009

This article is published under license to BioMed Central Ltd. 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.