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W. Elmenreich, R. Kirner:
"A Robust Certainty Grid Algorithm for Robotic Vision";
in: "Intelligent Systems at the Service of Mankind", UBooks Verlag, Augsburg, Deutschland, 2003, ISBN: 3-935798-25-3, S. 67 - 78.

In this paper we describe an algorithm for fault
tolerant sensor mapping for robotic vision. Basically, we use a
certainty grid algorithm to map distance measurements into a
two-dimensional grid. The well-know certainty grid algorithm can
tolerate occasional transient sensor errors and crash failures,
but will fail when a sensor provides permanently faulty
measurements.

Therefore we extended the certainty grid algorithm by a sensor
validation method that detects abnormal sensor measurements and
adjusts a confidence value for each sensor. This robust certainty
grid approach works with at least three sensors with an
overlapping sensing range and needs fewer sensor inputs and less
memory than other approaches. Our method supports also
reintegration of recovered sensors and sensor maintenance by
providing a measurement for the operability of a sensor.

We also present a case study with an autonomous mobile robot that
features the robust certainty grid algorithm in a time-triggered
architecture.

http://aleph.ub.tuwien.ac.at/F?base=tuw01&func=find-c&ccl_term=AC04968402