Andreas Nüchter, Hartmut Surmann,
Kai Lingemann, Joachim Hertzberg Fraunhofer Institute for Autonomous Intelligent Systems (AIS)
Schloss Birlinghoven
Sebastian Thrun Computer Science Department
Stanford University
Stanford, CA, USA
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.
