Efficient and Effective Multi-Camera Pose Estimation with Weighted M-Estimate Sample Consensus

Camera pose estimation is a fundamental module for many vision tasks. It is usually based on feature correspondences, i.e., feature matches across different images. However, correspondences always contain non-negligible outliers, which may negatively affect pose estimation efficiency and accuracy. This paper proposes a multi-camera pose estimation method by leveraging point and line correspondences with non-negligible outliers, in which a weighted M-Estimate Sample Consensus (w-MSAC) based on the customized weights and the coarse pose prior is introduced to improve the efficiency and accuracy of pose estimation. The customized weights could decrease the iterations of the pose hypothesis and improve the pose estimation accuracy. The coarse pose prior is used to perform the pre-validation of the pose hypothesis, eliminating many unnecessary validations. Experiments demonstrate the superiority of the proposed w-MSAC
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over existing state-of-the-art methods, e.g., improving 22% positioning and 24% orientation accuracy meanwhile decreasing 15% iterations and 92% validations than the MSAC.