6D SLAM with Approximate Data Association

Andreas Nüchter, Kai Lingemann, Joachim Hertzberg University of Osnabrück, Institute for Computer Science
Knowledge-Based Systems Research Group
Albrechtstraße 28
D-49069 Osnabrück, Germany {nuechtertex2html_wrap_inline$|$lingemanntex2html_wrap_inline$|$hertzberg} at informatik.uni-osnabrueck.de - Hartmut Surmann Fraunhofer Institute for
Autonomous Intelligent Systems (AIS)
Schloss Birlinghoven
D-53754 Sankt Augustin, Germany
hartmut.surmann at ais.fraunhofer.de

Abstract:

This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. A fast variant of the Iterative Closest Points (ICP) algorithm registers 3D scans taken by a mobile robot into a common coordinate system and thus provides relocalization. Hereby, data association is reduced to the problem of searching for closest points. Approximation algorithms for this searching, namely, approximate $k$d-trees and box decomposition trees, are presented and evaluated in this paper. A solution to 6D SLAM that considers all free parameters in the robot pose is built based on 3D scan matching.