Recent work in terrain classification has relied largely on 3D sensing methods and color based classification. We present an approach that works with a single, compact camera and maintains high classification rates that are robust to changes in illumination. Terrain is classified using a bag of visual words (BOVW) created from speeded up robust features (SURF) with a support vector machine (SVM) classifier. We present several novel techniques to augment this approach. A gradient descent inspired algorithm is used to adjust the SURF Hessian threshold to reach a nominal feature density. A sliding window technique is also used to classify mixed terrain images with high resolution. We demonstrate that our approach is suitable for small legged robots by performing real-time terrain classification on LittleDog. The classifier is used to select between predetermined gaits to traverse terrain of varying difficulty. Results indicate that real-time classification in-the-loop is faster than using a single all-terrain gait.

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