Bias reduction for stereo based motion estimation with applications to large scale visual odometry
conference paper
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.
TNO Identifier
248202
ISBN
9781424439935
Article nr.
5206519
Source title
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 20 - 25 June 2009, Miami, FL. USA
Pages
2222-2229
Files
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