Toward stochastic modeling of obstacle detectability in passive stereo range imagery

To design high-performance obstacle detection systems for semi-autonomous navigation, it will be necessary to characterize the performance of obstacle detection sensors in quantitative, statistical terms and to develop design methodologies that relate task requirements (e.g., vehicle speed) to sensor system parameters (e.g., image resolution). Steps to be taken to realize such a methodology are outlined. For the specific case of obstacle detection with passive stereo range imagery, the development of the statistical models needed for the methodology is begun, and experimental results for outdoor images of a gravel road, which test the models empirically, are presented. The experimental results show sample error distributions for estimates of disparity and range, illustrate systematic errors caused by partial occlusion, and demonstrate that effective obstacle detection is achievable.<>