Online probabilistic topological mapping

  • Authors:

  • Ananth Ranganathan;Frank Dellaert

  • Affiliations:

  • Honda Research Institute USA, Mountain View, CA, USA,;College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2011

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Abstract

We present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping (OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given measurements. With each successive measurement, the posterior is updated incrementally using a Rao芒聙聰Blackwellized particle filter. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies that further enable OPTM芒聙聶s operation in an online manner. OPTM can incorporate various sensors seamlessly, as is demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art significantly. We evaluate the algorithm on a robot in diverse environments.