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Citation
Jeremy Schiff, Erik Sudderth, Ken Goldberg. "Nonparametric Belief Propagation for Distributed Tracking of Robot Networks with Noisy Inter-Distance Measurements.". IEEE International Conference on Intelligent Robots and Systems (IROS)., October 2009.

Abstract
We consider the problem of tracking multiple moving robots using noisy sensing of inter-robot and interbeacon distances. Sensing is local: there are three fixed beacons at known locations, so distance and position estimates propagate across multiple robots. We show that the technique of Nonparametric Belief Propagation (NBP), a graph-based generalization of particle filtering, can address this problem and model multi-modal and ring-shaped uncertainty distributions. NBP provides the basis for distributed algorithms in which messages are exchanged between local neighbors. Generalizing previous approaches to localization in static sensor networks, we improve efficiency and accuracy by using a dynamics model for temporal tracking. We compare the NBP dynamic tracking algorithm with SMCL+R, a sequential Monte Carlo algorithm [1]. Whereas NBP currently requires more computation, it converges in more cases and provides estimates that are 3 to 4 times more accurate. NBP also facilitates probabilistic models of sensor accuracy and network connectivity.

Electronic downloads

• respectful-cameras-book-chapter-F08.pdf · application/pdf · 1760 kbytes

• HTML

  Jeremy Schiff, Erik Sudderth, Ken Goldberg. <a
  href="http://www.truststc.org/pubs/703.html"
  >Nonparametric Belief Propagation for Distributed
  Tracking of Robot Networks with Noisy Inter-Distance
  Measurements.</a>, <i>IEEE International
  Conference on Intelligent Robots and Systems
  (IROS).</i>, October 2009.

• Plain text

  Jeremy Schiff, Erik Sudderth, Ken Goldberg.
  "Nonparametric Belief Propagation for Distributed
  Tracking of Robot Networks with Noisy Inter-Distance
  Measurements.". <i>IEEE International Conference
  on Intelligent Robots and Systems (IROS).</i>, October
  2009.

• BibTeX

  @article{SchiffSudderthGoldberg09_NonparametricBeliefPropagationForDistributedTracking,
      author = {Jeremy Schiff and Erik Sudderth and Ken Goldberg},
      title = {Nonparametric Belief Propagation for Distributed
                Tracking of Robot Networks with Noisy
                Inter-Distance Measurements.},
      journal = {IEEE International Conference on Intelligent
                Robots and Systems (IROS).},
      month = {October},
      year = {2009},
      abstract = {We consider the problem of tracking multiple
                moving robots using noisy sensing of inter-robot
                and interbeacon distances. Sensing is local: there
                are three fixed beacons at known locations, so
                distance and position estimates propagate across
                multiple robots. We show that the technique of
                Nonparametric Belief Propagation (NBP), a
                graph-based generalization of particle filtering,
                can address this problem and model multi-modal and
                ring-shaped uncertainty distributions. NBP
                provides the basis for distributed algorithms in
                which messages are exchanged between local
                neighbors. Generalizing previous approaches to
                localization in static sensor networks, we improve
                efficiency and accuracy by using a dynamics model
                for temporal tracking. We compare the NBP dynamic
                tracking algorithm with SMCL+R, a sequential Monte
                Carlo algorithm [1]. Whereas NBP currently
                requires more computation, it converges in more
                cases and provides estimates that are 3 to 4 times
                more accurate. NBP also facilitates probabilistic
                models of sensor accuracy and network connectivity.},
      URL = {http://www.truststc.org/pubs/703.html}
  }
  

Posted by Jessica Gamble on 5 Apr 2010.
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