Accurate Object Localization in 3D Laser Range Scans

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 presents a novel method for object detection and classification in 3D laser range data that is acquired by an autonomous mobile robot. Unrestricted objects are learned using classification and regression trees (CARTs) and using an Ada Boost learning procedure. Off-screen rendered depth and reflectance images serve as an input for learning. The performance of the classification is improved by combining both sensor modalities, which are independent from external light. This enables highly accurate, fast and reliable 3D object localization with point matching. Competitive learning is used for evaluating the accuracy of the object localization.