Benjamin Kuipers and Patrick Beeson. 2002.
Bootstrap learning for place recognition.
Proceedings of the Eighteenth National Conference on
Artificial Intelligence (AAAI-02).
We present a method whereby a robot can learn to recognize places with
high accuracy, in spite of perceptual aliasing (different places
appear the same) and image variability (the same place appears
differently). The first step in learning place recognition restricts
attention to distinctive states identified by the map-learning
algorithm, and eliminates image variability by unsupervised learning
of clusters of similar sensory images. The clusters define
views associated with distinctive states, often increasing
perceptual aliasing. The second step eliminates perceptual aliasing
by building a causal/topological map and using history information
gathered during exploration to disambiguate distinctive states. The
third step uses the labeled images for supervised learning of direct
associations from sensory images to distinctive states. We evaluate
the method using a physical mobile robot in two environments, showing
high recognition rates in spite of large amounts of perceptual