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Robust Accurate Extrinsic Calibration of Static Non-overlapping Cameras

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Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10425))

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Abstract

An increasing number of robots and autonomous vehicles are equipped with multiple cameras to achieve surround-view sensing. The estimation of their relative poses, also known as extrinsic parameter calibration, is a challenging problem, particularly in the non-overlapping case. We present a simple and novel extrinsic calibration method based on standard components that performs favorably to existing approaches. We further propose a framework for predicting the performance of different calibration configurations and intuitive error metrics. This makes selecting a good camera configuration straightforward. We evaluate on rendered synthetic images and show good results as measured by angular and absolute pose differences, as well as the reprojection error distributions.

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Notes

  1. 1.

    https://github.com/midjji/non-overlapping-extrinsic-cameracalibration.git.

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Acknowledgement

This work was funded in part by Vinnova, Sweden’s innovation agency, through grant iQmatic, Daimler AG, EC’s Horizon 2020 Programme, grant agreement CENTAURO and The Swedish Research Council through a framework grant for the project Energy Minimization for Computational Cameras (2014-6227).

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Correspondence to Andreas Robinson .

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Robinson, A., Persson, M., Felsberg, M. (2017). Robust Accurate Extrinsic Calibration of Static Non-overlapping Cameras. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_29

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  • DOI: https://doi.org/10.1007/978-3-319-64698-5_29

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