Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors

Statistical modeling and evaluation of the performance of obstacle detection systems for unmanned ground vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. This issue is addressed for imaging range sensors by dividing the valuation problem into two levels, i.e., the quality of the range data itself and the quality of the obstacle detection algorithms applied to the range data. Existing models of the quality of range data from stereo vision and AM-CW laser range-finders (LADAR) are reviewed. These are used to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as functions of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. These models are evaluated experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors