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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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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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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