The usability of mobile robots for surveillance, search and rescue missions can be significantly improved by intelligent functionalities decreasing the cognitive load on the operator or even allowing autonomous operations, e.g., when communication fails. Mobility in this regard is not only a mechatronic problem but also a perception, modeling and planning challenge. Here, the perception issue of detecting drivable ground is addressed, an important issue for safety, security, and rescue robots, which have to operate in a vast range of unstructured, challenging environments. The simple yet efficient approach is based on the Hough transform of planes. The idea is to design the parameter space such that drivable surfaces can be easily detected by the number of hits in the bins corresponding to drivability. A decision tree International University Bremen until spring 2007on the bin properties increases robustness as it allows to handle uncertainties, especially sensor noise. In addition to the binary distinction of drivable/non-drivable ground, a classification of terrain types is possible. The algorithm is applied to 3D data obtained from two different sensors, namely, a time-of-flight camera and a stereo camera. Experimental results are presented for indoor and outdoor terrains, demonstrating robust realtime detection of drivable ground. Seven datasets recorded under very varying conditions are used. About 6,800 snapshots of range data are processed in total. It is shown that drivability can be robustly detected with success rates ranging between 83 % and 100%. Computation is extremely fast in the order of 5 to 50 msec. 1

Abstract—This paper describes a tactile probe designed for surface identification, in a context of all-terrain low-velocity mobile robotics. The proposed tactile probe is made of a small metallic rod with a single-axis accelerometer attached near its tip. Surface identification is based on analyzing acceleration patterns induced at the tip of this mechanically robust tactile probe, while it is passively dragged along a surface. A training data set was collected over ten different indoor and outdoor surfaces. Classification results for an artificial neural network were positive, with 89.9 % and 94.6 % success rate for 1 and 4 second time-windows of data, respectively. We also demonstrated that the same tactile probe can be used for unsupervised learning of terrains. For 1 second time-windows of data, the classification success rate was only reduced to 74.1%. Finally, a blind mobile robot, performing real-time classification of surfaces, demonstrated the feasibility of this tactile probe as a guidance mechanism.