Applying domain knowledge to slam using virtual measurements
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Abstract
Simultaneous Localization and Mapping (SLAM)
aims to estimate the maximum likelihood map and robot
pose based on a robot’s control and sensor measurements.
In structured environments, such as human environments, we
might have additional domain knowledge that could be applied
to produce higher quality mapping results.We present a method
for using virtual measurements, which are measurements between
two features in our map. To demonstrate this, we present
a system that uses such virtual measurements to relate visually
detected points to walls detected with a laser scanner.
Sponsor
This research has in part been sponsored by ARL MAST CTA, the GT-Boeing Manufacturing Initiative, and the KORUS Cognitive Home Robot
Project.
Date
2010-05
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