Improvements in accuracy of single camera terrain classification

Autonomous terrain classification is an important requirement for robotic applications for the outdoor and more so for off-road systems. Different technique have been developed in recent years mainly relying on either color features or on texture-based features for classification. We present an approach which combines the two approaches and delivers an overall increase in performance and accuracy. We describe the computational framework, training dataset, off-line learning and real-time classification results of our system. We report overall average classification accuracies in excess of 98% in a fair experimental setup along with confusion matrices. Our method gives a noticeable improvement in accuracy for classifying similar terrain classes over the current state of the art that uses only texture for classification with acceptable overhead for real-time applications.