Improving robustness for real-time vehicle egomotion estimation

Knowledge about the host egomotion can help to stabilize and improve many applications in the advanced driver assistance domain. It can be a crucial feature for object tracking and calibration. In this paper we describe a novel approach which is fast to compute and robust. We utilize a depth prior for the translation and integrate robust estimation techniques, like MSAC and an M-estimator. The MSAC is further improved by imposing prior information directly into the MSAC step. We can show that using this scheme is fast and enhances our results. For testing we utilize a large video dataset from which we also have computed the pose estimates via sparse bundle adjustment. Using a loop-closing sequence we also qualitatively analyze our results. The presented approach has been tested online on a car PC and as such can be computed in real time.