Stereo based visual odometry in difficult traffic scenes

Reliable vehicle ego motion estimation based on visual information is an important research goal because it has applications like accurate long term localization by fusion with other sensors, temporal fusion between frames, moving obstacles detection and tracking, path planning etc. This paper evaluates and significantly improves some steps of existing visual odometry methods. The main contribution is related to accuracy improvements in case of illumination changes by using the rank transform. Additionally we propose a new consistency check, based on image deformations, for subsets of features considered during the RANSAC iterations of the algorithm. Performance of GPU execution and results in various traffic scenarios are presented in order to show the advantages and the robustness of the method.