Detection and tracking of independently moving objects in urban environments

In this paper we propose an approach for dynamic scene perception from a moving vehicle equipped with a stereo camera rig. The approach is solely based on visual information, hence it is applicable to a large class of autonomous robots working in indoor as well as in outdoor environments. The proposed approach consists of an egomotion estimation based on disparity and optical flow using the Longuet-Higgins-Equations combined with an implicit extended Kalman-Filter. Based on this egomotion estimation a moving object detection and tracking is performed. Each tracked object is labeled with a unique ID while visible in the images. The proposed algorithm was evaluated on numerous challenging real world image sequences.