FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

  • Authors:

  • Mark Cummins;Paul Newman

  • Affiliations:

  • Mobile Robotics Group, University of Oxford, UK;Mobile Robotics Group, University of Oxford, UK

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2008

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Abstract

This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mobile robotics.