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Planar Motion Estimation for Multi-camera System

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13188))

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Abstract

In this paper, we propose efficient solutions to relative pose estimation with a multi-camera system. We focus on the case where the system navigates under planar motion, and propose two new algorithms: the non-minimal linear 6-point algorithm and the minimal 3-point algorithm. For the 6-point algorithm, we use a simple and easy-to-implement way to avoid the SVD (singular value decomposition) induced degenerate configuration, which happens in the multi-camera system based relative pose estimation. The minimal 3-point algorithm results in a system of polynomials with respect to three unknowns, and we show that it can be converted to solve a univariate polynomial in degree 4. The proposed algorithms are compared with the state-of-the-art methods on both synthetic data and public real-world images. Experimental results show very promising performance in terms of accuracy, robustness and efficiency.

This work was supported by Shanghai Automotive Industry Science and Technology Development Fundation (No. 1917).

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Qi, X., Ding, Y., Xie, J., Yang, J. (2022). Planar Motion Estimation for Multi-camera System. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_9

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  • DOI: https://doi.org/10.1007/978-3-031-02375-0_9

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