Bootstrap learning for place recognition
Benjamin Kuipers and Patrick Beeson. Bootstrap learning for place recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 174–180, Edmonton, Canada, July 2002.
Abstract
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 aliasing.
Additional Information
One of only 29 papers selected for oral presentation at AAAI 2002.
Talk slides
BibTeX
@InProceedings{Kuipers-aaai-02,
author = {Benjamin Kuipers and Patrick Beeson},
title = {Bootstrap learning for place recognition},
booktitle = {Proceedings of the AAAI Conference on Artificial
Intelligence},
year = 2002,
address = {Edmonton, Canada},
month = {July},
pages = {174--180},
abstract = {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 aliasing.},
bib2html_pubtype ={Refereed Conference},
bib2html_rescat ={Foundational Learning},
bib2html_extra_info ={One of only 29 papers selected for oral
presentation at AAAI 2002.<br><br><a
href="http://personal.traclabs.com/~pbeeson/talks/Kuipers-aaai-02_talk.pdf">
Talk slides</a>},
}