Abstract
This article presents an overview over the whole process of generating a precise and rich 3D representation of the local environment of a moving mobile outdoor robot. The resulting model of this process is a camera image textured triangle mesh which is triangulated from a motion-corrected laser scanned 3D point cloud. The demanding requirements of autonomous off-road environment model acquisition are handled by applying a multi-sensor fusion approach. Additionally to the model creation process a novel way of detecting and describing feature points on a piece-wise linear 3D surfaces is presented. The set of feature points generated by the proposed method is a valuable abstraction of a whole outdoor scene that can be used in several following processing steps like mesh simplification or segmentation and robotics-specific tasks like obstacle detection or classification. All described methods are implemented and successfully used on the award-winning robot AMOR.
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