Andreas Nüchter, Hartmut Surmann, Joachim Hertzberg
Fraunhofer Institute for Autonomous Intelligent Systems (AIS)
Schloss Birlinghoven
D-53754 Sankt Augustin, Germany
{nuechter,surmann,hertzberg}@ais.fraunhofer.de
This paper presents a new method for object detection and
classification in 3D laser range data that is acquired by an
autonomous mobile robot. Off-screen rendered depth and
reflectance images serve as an input for an Ada Boost learning
procedure that constructs a cascade of classifiers. The
performance of the classification is improved by combining both
sensor modalities, which are independent from external light. The
resulting approach for object classification is real-time capable
and reliable. It combines recent results in computer vision with
the emerging technology of 3D laser scanners.
