Toward bootstrap learning for place recognition

Benjamin Kuipers and Patrick Beeson. Toward bootstrap learning for place recognition. In Symposium on Anchoring Symbols to Sensory Data in Single and Multiple Robot Systems, AAAI Fall Symposium Series, North Falmouth, MA, November 2001. AAAI Technical Report FS-01-01.

Abstract

We present a method whereby a robot with no prior knowledge of its sensors, effectors or environment 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). Previous work showed how such a robot could learn from its experience a useful set of sensory features, motion primitives, and local control laws to move from one distinctive state to another. Such progressive learning of a hierarchical representation is called bootstrap learning. The first step in learning place recognition eliminates image variability in two steps: (a) focusing on recognition of distinctive states defined by the robot's control laws, and (b) 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 cognitive 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 large amounts of perceptual aliasing and high resulting recognition rates.

Additional Information

Also see: Benjamin Kuipers and Patrick Beeson. Bootstrap learning for place recognition. AAAI Conference on Artificial Intelligence, 2002.

BibTeX

@InProceedings{Kuipers-fss-01,
  author =       {Benjamin Kuipers and Patrick Beeson},
  title =        {Toward bootstrap learning for place recognition},
  booktitle =    {Symposium on Anchoring Symbols to Sensory Data in Single and
                  Multiple Robot Systems},
  year =         2001,
  series =       {AAAI Fall Symposium Series},
  address =      {North Falmouth, MA},
  month =        {November},
  note =         {AAAI Technical Report FS-01-01.},
  abstract =     {We present a method whereby a robot with no prior knowledge
                  of its sensors, effectors or environment 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). Previous
                  work showed how such a robot could learn from its experience
                  a useful set of sensory features, motion primitives, and
                  local control laws to move from one distinctive state to
                  another. Such progressive learning of a hierarchical
                  representation is called bootstrap learning. The first step
                  in learning place recognition eliminates image variability
                  in two steps: (a) focusing on recognition of distinctive
                  states defined by the robot's control laws, and (b)
                  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 cognitive 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 large
                  amounts of perceptual aliasing and high resulting
                  recognition rates.},
  bib2html_pubtype ={Workshop},
  bib2html_rescat ={Foundational Learning},
  bib2html_extra_info ={Also see: Benjamin Kuipers and Patrick Beeson. <a
                  href="http://personal.traclabs.com/~pbeeson/publications/b2hd-Kuipers-aaai-02.html">
                  Bootstrap learning for place recognition</a>. AAAI
                  Conference on Artificial Intelligence, 2002.}
}

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