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Abstract The usability of mobile robots for surveillance and search and rescue missions can be significantly improved by adding intelligent functionalities that decrease the cognitive load on the operator or that even allow autonomous behavior of the robots, e.g., when communication fails. Mobility in this regard is not only the mechatronic endeavor of developing suited locomotion apparatuses, but also a perception, modeling, and planning challenge. Here, the perception issue of detecting drivable ground is addressed, a particularly 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 on the bin properties increases robustness as it allows the robot to handle uncertainties, especially sensor noise. In addition to the binary distinction of drivable/nondrivable ground, a classification of terrain types is possible. The algorithm is applied to 3D data obtained from two different sensors, namely, a near‐infrared time of flight camera and a stereo camera. Experimental results are presented for typical indoor as well as outdoor terrains, demonstrating robust realtime detection of drivable ground. Seven datasets recorded indoors and outdoors under very varying conditions are used for this purpose. About 6800 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 ms. © 2008 Wiley Periodicals, Inc.
Poppinga et al. (Fri,) studied this question.