Obstacle detection and recognition in natural terrain for field mobile robot navigation
This paper presents a novel method for real-time obstacle detection and recognition in natural terrain for a field mobile robot using a image information, geometric information and support vector machine(SVM). Firstly, the scene is divided into two distinct regions: interest regions and uninterested regions. Then detected obstacle points are clustered into objects on the basis of their geometric information, i.e., depth and horizontal information, connectivity. The key obstacle characteristics are identified as width, height, their ratio, the ratio of area and depth. In the paper, the SVM method is used to classifying the objects into four classes. In order to determine the slope value, a SVM slope estimation approximation model was also proposed. Experimental results are presented to demonstrate the capability of the proposed approach for recognition of different obstacle in natural terrain.