Abstract
To create with an autonomous mobile robot a 3D
volumetric map of a scene it is necessary to gage
several 3D scans and to merge them into one
consistent 3D model. This paper provides a new
solution to the simultaneous localization and
mapping (SLAM) problem with six degrees of
freedom. Robot motion on natural surfaces has to
cope with yaw, pitch and roll angles, turning pose
estimation into a problem in six mathematical
dimensions. A fast variant of the Iterative Closest
Points algorithm registers the 3D scans in a common
coordinate system and relocalizes the
robot. Finally, consistent 3D maps are generated
using a global relaxation. The algorithms have been
tested with 3D scans taken in the Mathies mine,
Pittsburgh, PA. Abandoned mines pose significant
problems to society, yet a large fraction of them
lack accurate 3D maps.
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