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Robust Accurate Extrinsic Calibration of Static Non-overlapping Cameras
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
2017 (English)In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10425, p. 342-353Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10425, p. 342-353
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10425
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-145371DOI: 10.1007/978-3-319-64698-5_29ISI: 000432084600029ISBN: 9783319646978 (print)ISBN: 9783319646985 (electronic)OAI: oai:DiVA.org:liu-145371DiVA, id: diva2:1185614
Conference
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II
Note

Funding agencies: Vinnova, Swedens innovation agency; Daimler AG; EC; Swedish Research Council [2014-6227]

Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-10-11Bibliographically approved

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Robinson, AndreasPersson, MikaelFelsberg, Michael

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