Vision-based obstacle detection using a support vector machine

Abstract

This paper describes a monocular vision-based obstacle detection method for a mobile robot using a support vector machine (SVM). A single camera is mounted on the front of a mobile robot and an SVM is trained to classify obstacles as they are encountered by the robot. Since it is not possible to train on all obstacle types a-priori, a one-class SVM is used to learn the appearance of the floor in the absence of obstacles. Anything that is not recognized as a floor is classified as an obstacle. To improve robustness in recognizing floor features, images are preprocessed using a Fast Fourier Transform (FFT) to provide translation invariance. Experimental results indicate high accuracy and specificity for four different floor surfaces that were tested.