Real-time obstacle detection in complex scenarios using dense stereo vision and optical flow

Mobile robots as well as tomorrows intelligent vehicles acting in complex dynamic environments must be able to detect both static and moving obstacles. In intersections or crowded urban areas this task proves to be highly demanding. Stereo vision has been extensively used for this task, as it provides a large amount of data. Since it does not reveal any motion information, static and dynamic objects immediately next to each other, or closely positioned obstacles moving in different directions are often merged into a single obstacle. In this paper we address these problems through a powerful fusion between 3D position information delivered by the stereo sensor and 3D motion information, derived from optical flow, in a depth-adaptive occupancy grid. The proposed model is presented and then applied for determining obstacle localization, orientation and speed.