Abstract
We present a study of minimal-case motion estimation with affine correspondences and introduce a new solution for multi-camera motion estimation with affine correspondences. Ego-motion estimation using one or more cameras is a well-studied topic with applications in 3D reconstruction and mobile robotics. Most feature-based motion estimation techniques use point correspondences. Recently, several researchers have developed novel epipolar constraints using affine correspondences. In this paper, we extend the epipolar constraint on affine correspondences to the multi-camera setting and develop and evaluate a novel minimal solver using this new constraint. Our solver uses six affine correspondences in the minimal case, which is a significant improvement over the point-based version that requires seventeen point correspondences. Experiments on synthetic and real data show that, in comparison to the point-based solver, our affine solver effectively reduces the number of RANSAC iterations needed for motion estimation while maintaining comparable accuracy.
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