Robust Optimal Pose Estimation

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Robust Optimal Pose Estimation
Abstract. We study the problem of estimating the position and orientation of a calibrated camera from an image of a known scene. A common problem in camera pose estimation is the existence of false correspondences between image features and modeled 3D points. Existing techniques such as ransac to handle outliers have no guarantee of optimality. In contrast, we work with a natural extension of the L norm to the outlier case. Using a simple result from classical geometry, we derive necessary conditions for L optimality and show how to use them in a branch and bound setting to find the optimum and to detect outliers. The algorithm has been evaluated on synthetic as well as real data showing good empirical performance. In addition, for cases with no outliers, we demonstrate shorter execution times than existing optimal algorithms.
Olof Enqvist, Fredrik Kahl
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2008
Where ECCV
Authors Olof Enqvist, Fredrik Kahl
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