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Flexible Photogrammetric Computations Using Modular Bundle Adjustment: The Chain Rule and the Collinearity Equations
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-7657-6917
Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, France.ORCID iD: 0000-0002-3310-8937
Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, France.ORCID iD: 0000-0002-7292-2755
3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy; COMEX SA–Innovation Department, COMEX, Marseille, France.
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2019 (English)In: Photogrammetric Engineering and Remote Sensing, ISSN 0099-1112, Vol. 85, no 5, p. 361-368Article in journal (Refereed) Published
Abstract [en]

The main purpose of this article is to show that photogrammetric bundle-adjustment computations can be sequentially organized into modules. Furthermore, the chain rule can be used to simplify the computation of the analytical Jacobians needed for the adjustment. Novel projection models can be flexibly evaluated by inserting, modifying, or swapping the order of selected modules. As a proof of concept, two variants of the pinhole projection model with Brown lens distortion were implemented in the open-source Damped Bundle Adjustment Toolbox and applied to simulated and calibration data for a nonconventional lens system. The results show a significant difference for the simulated, error-free, data but not for the real calibration data. The current flexible implementation incurs a performance loss. However, in cases where flexibility is more important, the modular formulation should be a useful tool to investigate novel sensors, data-processing techniques, and refractive models.

Place, publisher, year, edition, pages
Bethesda, Maryland, USA: American Society of Photogrammetry and Remote Sensing (ASPRS) , 2019. Vol. 85, no 5, p. 361-368
Keywords [en]
Bundle adjustment, photogrammetry, chain rule, software
National Category
Computational Mathematics Probability Theory and Statistics
Research subject
computer and systems sciences; Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-157690DOI: 10.14358/PERS.85.5.361ISI: 000468866900006OAI: oai:DiVA.org:umu-157690DiVA, id: diva2:1300908
Available from: 2019-04-01 Created: 2019-04-01 Last updated: 2019-06-17Bibliographically approved

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Börlin, NiclasNocerino, Erica

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Börlin, NiclasMurtiyoso, ArnadiGrussenmeyer, PierreNocerino, Erica
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