Classifying terrain properties for planetary exploration rovers based on a combined distance measure with dynamic texture model

Non-geometric terrain properties surrounding a planetary exploration rover can be exploited to improve autonomous mobility of the rover. This paper shows a method to classify non-geometric terrain properties using image sequences obtained from onboard cameras. Our method is based on a Dynamic Texture analysis, which is a technique to estimate scene motion in image sequences. Using Dynamic Textures, we can incorporate a motion cue to classify not only soil types but also the velocities of a rover relative to terrain surface representing slippage due to terrains. First, we briefly show a learning algorithm for Dynamic Textures. Then we propose a combined distance measure for classification. Combining distance measures is useful to handle different properties of terrain, that is, a static property such as soil types and a dynamic property such as the velocities. The effectiveness of the combined distance measure for improving classification performance is demonstrated through experimental runs of two rover testbeds on sand pits.