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