| Volume 27 Issue 6 - Publication Date: 1 June 2008 |
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| FAB-MAP: Probabilistic
Localization and
Mapping in the Space of
Appearance |
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| Mark Cummins and Paul Newman
Mobile Robotics Group, University of Oxford, UK |
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| 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. |
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| Multimedia Key |
= Video |
 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
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Example
1: Results for the New College Dataset. (48.2 MB) avi |
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Example
2: Results for the City Centre Dataset. (35.1 MB) avi |
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Example
3: Images, GPS coordinates and ground
truth labels. (9 KB) htm |
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Example
4: Generalization Performance – Indoor
Dataset A. (7.26 MB) avi |
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5 |
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Example
5: Generalization Performance – Indoor
Dataset B. (3.16 MB) avi |
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