Extracting Terrain Features from Range Images for Autonomous Random Stepfield Traversal

One of the challenges of rescue robotics is to create robots that can autonomously traverse rough, unstructured terrain. Although mechanical engineering can produce very capable robots, mechanical engineering alone will not drive them. In this paper, we present a terrain feature extractor that can be taught to find significant features in range images of terrain around a robot from a human expert. This novel approach has the advantage that it potentially allows the human expert's knowledge to be captured rapidly. A terrain model is generated from the many points in the range sensor data. Techniques from the field of knowledge acquisition are then used to find patterns in the terrain model. A knowledge acquisition system can then be taught to drive a robot in unstructured terrain based on these features. We evaluate the performance of the initial stages of the feature extractor on a real robot, traversing NIST specification red stepfields.