A New Approach for Terrain Analysis in Mobile Robot Applications

This paper presents a novel approach to detect traversable and non-traversable regions of the environment from a depth image that could enhance mobility and safety of mobile robots through integration with localization, control and planning methods. The proposed system is based on Principal Component Analysis (PCA). PCA theory provides a powerful means to analyze 3D surfaces widely used in computer vision. It can be successfully applied, as well, to increase the degree of perception in autonomous vehicles, as new generations of 3D imaging sensors, including stereo and RGB-D-cameras, are increasingly introduced. The approach described in this paper is based on the estimation of the normal vector to a local surface leading to the definition of a novel, so-called, Unevenness Point Descriptor. Experimental results, obtained from indoor and outdoor environments, are presented to validate the system. It is demonstrated that the proposed approach can be effectively used for scene segmentation and it can efficiently handle difficult scenarios, including the presence of terrain slopes.