A novel approach of terrain classification for outdoor automobile navigation

One of the main challenges for autonomous navigation is to determine which obstacles can be driven over and which need to be avoided. The investigation of reconstructing 3D model of the viewed scene has showed good performance in environments such as in yard, hall way or on road. However, in cluttered outdoor environments where frequently the scenes are unknown and the objects are no more static and rigid, the only use of 3D-point analysis is not sufficient to give good decision for safe navigation. Therefore, we on the other hand address a new approach which reconstructs completely 3D scene based on calibrating Laser scanner and CMOS camera and doing segmentation to result objects in form of region of interest. As a result, the characteristics of each region are then expressed through their corresponding feature vectors, including 2D and 3D features. This is the first time the term of feature vector used to describe a 3D object respecting to the analysis of 3D-point clouds given by a Ladar. Finally, we also prove that the proposed approach leads to more robust and faster processing and decision-making in terrain classification compared with conventional approaches or pixel-based approaches.