Information-Theoretic Mapping Using Cauchy-Schwarz Quadratic Mutual Information
Benjamin Charrow, Sikang Liu, Vijay Kumar, Nathan Michael

Citation
Benjamin Charrow, Sikang Liu, Vijay Kumar, Nathan Michael. "Information-Theoretic Mapping Using Cauchy-Schwarz Quadratic Mutual Information". International Conference on Robotics and Automation (ICRA), 26, May, 2015.

Abstract
We develop a computationally efficient control policy for active perception that incorporates explicit models of sensing and mobility to build 3D maps with ground and aerial robots. Like previous work, our policy maximizes an information-theoretic objective function between the discrete occupancy belief distribution (e.g., voxel grid) and future measurements that can be made by mobile sensors. However, our work is unique in three ways. First, we show that by using Cauchy-Schwarz Quadratic Mutual Information (CSQMI), we get significant gains in efficiency. Second, while most previous methods adopt a myopic, gradient-following approach that yields poor convergence properties, our algorithm searches over a set of paths 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. Third, because we consider models of sensing and mobility, our method naturally applies to both ground and aerial vehicles. The paper describes the basic models, the problem formulation and the algorithm, and demonstrates applications via simulation and experimentation.

Electronic downloads

Internal. This publication has been marked by the author for TerraSwarm-only distribution, so electronic downloads are not available without logging in.

Citation formats

• HTML

  Benjamin Charrow, Sikang Liu, Vijay Kumar, Nathan Michael.
  <a
  href="http://www.terraswarm.org/pubs/515.html"
  >Information-Theoretic Mapping Using Cauchy-Schwarz
  Quadratic Mutual Information</a>, International
  Conference on Robotics and Automation (ICRA), 26, May, 2015.

• Plain text

  Benjamin Charrow, Sikang Liu, Vijay Kumar, Nathan Michael.
  "Information-Theoretic Mapping Using Cauchy-Schwarz
  Quadratic Mutual Information". International Conference
  on Robotics and Automation (ICRA), 26, May, 2015.

• BibTeX

  @inproceedings{CharrowLiuKumarMichael15_InformationTheoreticMappingUsingCauchySchwarzQuadratic,
      author = {Benjamin Charrow and Sikang Liu and Vijay Kumar
                and Nathan Michael},
      title = {Information-Theoretic Mapping Using Cauchy-Schwarz
                Quadratic Mutual Information},
      booktitle = {International Conference on Robotics and
                Automation (ICRA)},
      day = {26},
      month = {May},
      year = {2015},
      abstract = {We develop a computationally efficient control
                policy for active perception that incorporates
                explicit models of sensing and mobility to build
                3D maps with ground and aerial robots. Like
                previous work, our policy maximizes an
                information-theoretic objective function between
                the discrete occupancy belief distribution (e.g.,
                voxel grid) and future measurements that can be
                made by mobile sensors. However, our work is
                unique in three ways. First, we show that by using
                Cauchy-Schwarz Quadratic Mutual Information
                (CSQMI), we get significant gains in efficiency.
                Second, while most previous methods adopt a
                myopic, gradient-following approach that yields
                poor convergence properties, our algorithm
                searches over a set of paths 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.
                Third, because we consider models of sensing and
                mobility, our method naturally applies to both
                ground and aerial vehicles. The paper describes
                the basic models, the problem formulation and the
                algorithm, and demonstrates applications via
                simulation and experimentation.},
      URL = {http://terraswarm.org/pubs/515.html}
  }
  

Posted by Barb Hoversten on 17 Mar 2015.
Groups: services