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The International Journal of Robotics Research
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FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

Mark Cummins

Mobile Robotics Group, University of Oxford, UK, mjc{at}robots.ox.ac.uk

Paul Newman

Mobile Robotics Group, University of Oxford, UK, pnewman{at}robots.ox.ac.uk

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.

Key Words: place recognition • topological SLAM • appearance based navigation

The International Journal of Robotics Research, Vol. 27, No. 6, 647-665 (2008)
DOI: 10.1177/0278364908090961
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P. Newman, G. Sibley, M. Smith, M. Cummins, A. Harrison, C. Mei, I. Posner, R. Shade, D. Schroeter, L. Murphy, et al.
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[Abstract] [PDF]