Bias reduction for stereo based motion estimation with applications to large scale visual odometry

This contribution addresses the problem of bias in stereo based motion estimation. Using a biased estimator within a visual-odometry system will cause significant drift on large trajectories. This drift is often minimized by exploiting auxiliary sensors, (semi-)global optimization or loop-closing. In this paper it is shown that bias in the motion estimates can be caused by incorrect modeling of the uncertainties in landmark locations. Furthermore, there exists a relation between the bias, the true motion and the distribution of landmarks in space. Guided by these observations, a novel bias reduction technique has been developed. The core of the proposed method is computing the difference between motion estimates obtained using dissimilar heteroscedastic landmark uncertainty models. This approach is accurate, efficient and does not rely on auxiliary sensors, (semi-)global optimization or loop-closing. To show the real-world applicability of the proposed method, it has been tested on several data-sets including a challenging 5 km urban trajectory. The gain in performance is clearly noticeable.