Positive and Negative Obstacle Detection using the HLD Classifier

Autonomous robots must be able to detect hazardous terrain even when sensor data is noisy and incomplete. In particular, negative obstacles such as cliffs or stairs often cannot be sensed directly; rather, their presence must be inferred. In this paper, we describe the height-length-density (HLD) terrain classifier that generalizes some prior methods and provides a unified mechanism for detecting both positive and negative obstacles. The classifier utilizes three novel features that inherently deal with partial observability. The structure of the classifier allows the system designer to encode the capabilities of the vehicle as well as a notion of risk, making our approach applicable to virtually any vehicle. We evaluate our method in an indoor/outdoor environment, which includes several perceptually difficult real-world cases, and show that our approach out-performs current methods.