FAB-MAP: Probabilistic localization and mapping in the space of appearance

Journal article

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. © 2008 SAGE Publications.

Authors: Cummins, M; Newman, P

• Publication status: Published
• Journal: INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
• Volume: 27
• Issue: 6
• Pages: 647-665
• Publication date: 2008-06-01
• DOI: 10.1177/0278364908090961
• EISSN: 1741-3176
• ISSN: 0278-3649
• Language: English
• Keywords: appearance based navigation; topological SLAM; place recognition