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Prioritized Multihypothesis Tracking by a Robot with Limited Sensing

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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Correspondence to Paul E. Rybski.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Rybski, P.E., Veloso, M.M. Prioritized Multihypothesis Tracking by a Robot with Limited Sensing. EURASIP J. Adv. Signal Process. 2009, 284525 (2009). https://doi.org/10.1155/2009/284525

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Keywords

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