Active Information-Based Localization and Mapping
Philip Dames, Benjamin Charrow, Sikang Liu, Nathan Michael, Vijay Kumar

Citation
Philip Dames, Benjamin Charrow, Sikang Liu, Nathan Michael, Vijay Kumar. "Active Information-Based Localization and Mapping". Talk or presentation, 30, October, 2014; Poster presented at the 2014 TerraSwarm Annual Meeting.

Abstract
In this work we seek to enable a team of mobile robots to autonomously gather information in an environment to relay to a remote human operator. We consider two key problems: 1) detecting and localizing an uknown number of objects of interest within a known environment and 2) generating a 3D occupancy grid of an unknown environment using a heterogeneous team of ground and aerial robots. We develop computationally efficient control policies for active perception that incorporate explicit models of sensing and mobility. Like previous work, our policy maximizes an information-theoretic objective function between the distribution over the targets/map and future measurements that can be made by mobile sensors. While most previous methods adopt a myopic, gradient-following approach that yields poor convergence properties, our algorithm searches over a set of paths generated over a range of length scales and is less susceptible to local minima. In doing so, we explicitly incorporate models of sensors, and model the dependence (and independence) of measurements over multiple time steps in a path. Because we consider models of sensing and mobility, our method naturally applies to both ground and aerial vehicles. We present results from simulated and hardware experiments that demonstrate the effectiveness of the proposed control policy.

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Citation formats  
  • HTML
    Philip Dames, Benjamin Charrow, Sikang Liu, Nathan Michael,
    Vijay Kumar. <a
    href="http://www.terraswarm.org/pubs/451.html"><i>Active
    Information-Based Localization and
    Mapping</i></a>, Talk or presentation,  30,
    October, 2014; Poster presented at the <a
    href="http://www.terraswarm.org/conferences/14/annual"
    >2014 TerraSwarm Annual Meeting</a>.
  • Plain text
    Philip Dames, Benjamin Charrow, Sikang Liu, Nathan Michael,
    Vijay Kumar. "Active Information-Based Localization and
    Mapping". Talk or presentation,  30, October, 2014;
    Poster presented at the <a
    href="http://www.terraswarm.org/conferences/14/annual"
    >2014 TerraSwarm Annual Meeting</a>.
  • BibTeX
    @presentation{DamesCharrowLiuMichaelKumar14_ActiveInformationBasedLocalizationMapping,
        author = {Philip Dames and Benjamin Charrow and Sikang Liu
                  and Nathan Michael and Vijay Kumar},
        title = {Active Information-Based Localization and Mapping},
        day = {30},
        month = {October},
        year = {2014},
        note = {Poster presented at the <a
                  href="http://www.terraswarm.org/conferences/14/annual"
                  >2014 TerraSwarm Annual Meeting</a>.},
        abstract = {In this work we seek to enable a team of mobile
                  robots to autonomously gather information in an
                  environment to relay to a remote human operator.
                  We consider two key problems: 1) detecting and
                  localizing an uknown number of objects of interest
                  within a known environment and 2) generating a 3D
                  occupancy grid of an unknown environment using a
                  heterogeneous team of ground and aerial robots. We
                  develop computationally efficient control policies
                  for active perception that incorporate explicit
                  models of sensing and mobility. Like previous
                  work, our policy maximizes an
                  information-theoretic objective function between
                  the distribution over the targets/map and future
                  measurements that can be made by mobile sensors.
                  While most previous methods adopt a myopic,
                  gradient-following approach that yields poor
                  convergence properties, our algorithm searches
                  over a set of paths generated over a range of
                  length scales and is less susceptible to local
                  minima. In doing so, we explicitly incorporate
                  models of sensors, and model the dependence (and
                  independence) of measurements over multiple time
                  steps in a path. Because we consider models of
                  sensing and mobility, our method naturally applies
                  to both ground and aerial vehicles. We present
                  results from simulated and hardware experiments
                  that demonstrate the effectiveness of the proposed
                  control policy.},
        URL = {http://terraswarm.org/pubs/451.html}
    }
    

Posted by Philip Dames on 7 Nov 2014.
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