The system is capable of detecting objects as small as 14cm high at ranges well in excess of 100m. The obstacle detection system described here relies on several factors. First, the camera system is configured in such a way that even small obstacles generate detect-able range measurements. This is done by using a very long baseline, telephoto lenses, and rigid camera mounts. Second, extremely accurate calibration procedures allow accurate determination of these range differences.
Multibaseline stereo is used to reduce the number of false matches and to improve range accuracy.
Special image filtering techniques are used to enhance the very weak image textures present on the road surface, reducing the number of false range measurements. Finally, a technique for determining the surface orientation directly from stereo data is used to detect the presence of obstacles. A system to detect obstacles is not useful if it does not run in near real-time. In order to improve performance, this dissertation includes a detailed analysis of each stage of the stereo algorithm. An efficient method for rectifying images for trinocular stereo is presented.