Automatic Classification of Objects in 3D Laser Range Scans
Year: 2004
Type: article
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.
Institution: Fraunhofer-Gesellschaft
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W95755733