Adaptive Essential Matrix Based Stereo Visual Odometry with Joint Forward-Backward Translation Estimation

Visual Odometry is widely used for recovering the trajectory of a vehicle in an autonomous navigation system. In this paper, we present an adaptive stereo visual odometry that separately estimates the rotation and translation. The basic framework of VISO2 is used here for feature extraction and matching due to its feature repeatability and real-time speed on standard CPU. The rotation is accurately obtained from the essential matrix of every two consecutive frames in order to avoid the affection of the stereo calibration uncertainty. With the estimated rotation, translation is rapidly calculated and refined by our proposed linear system with non-iterative refinement without the requirement of any ground truth data. The further improvement of the translation by joint backward and forward estimation is also presented in the same framework of the proposed linear system. The experimental results evaluated on the KITTI dataset demonstrate around 30% accuracy enhancement of the proposed scheme over the traditional visual odometry pipeline without much increase in the system overload. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.