Automatic Classification of Objects in 3D Laser Range Scans

Abstract

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.