Effects of Environment Model Parametrization on Photogrametry Reconstruction

Jan Klecka

doi:10.13164/mendel.2018.1.151

Jan Klecka

DOI: https://doi.org/10.13164/mendel.2018.1.151

Keywords: Photogrammetry, 3D reconstruction, Multiple view reconstruction, recurrent estimation, maximum likelihood, Extended Kalman filter

Abstract

This paper is aimed at a description of effects which have assumptions of specific environment structure on quality of recurrently conducted photogrammetry reconstruction. The theoretical part covers the description of three different assumptions of environment structure and mathematical derivation of two suitable recurrent estimators: one based on Extended Kalman filter and the second one based on Maximum likelihood method. The experimental part is introducing simple virtual environment which is observed by linear camera model and then reconstructed using predefined algorithms and assumptions.

References

Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2003).

Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle Adjustment — A Modern Synthesis. In: Vision Algorithms: Theory and Practice. pp. 298-372. Springer Berlin Heidelberg, Berlin, Heidelberg (2000). DOI: 10.1007/3-540-44480-7_21.

Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2o: A general framework for graph optimization. In: IEEE International Conference on Robotics and Automation (ICRA). pp. 3607-3613. IEEE Publishing (2011). DOI: 10.1109/ICRA.2011.5979949.

Horak, K., Richter, M., Kalova, I.: Human eyes localization for driver inattention monitoring system. In: R. Matousek (ed.), In MENDEL, (MENDEL 2009), vol. 15, No. 1, pp. 283-288, Brno University of Technology, Brno (June 2009), ISSN 1803-3814

Horak, K., Richter, M., Kalova, I.: Automated Flaws Detection on Bottles in Food Industry. Annals of DAAAM & Proceedings. 20, 133-133 (2009).

Jelinek, A., Zalud, L., Jilek, T.: Fast total least squares vectorization. Journal of Real-Time Image Processing. 1–17 (2016). DOI: 10.1007/s11554-016-0562-6.

Prochazkova, J., Kratochvil, J.: Direct point cloud visualization using T-spline with edge detection. In: Advances in Intelligent Systems and Computing. 240-251. Springer Verlag (2017). DOI: 10.1007/978-3-319-58088-3_23

Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: Large-Scale Direct Monocular SLAM. Computer Vision – ECCV 2014. 834 (2014). DOI: 10.1007/978-3-319-10605-2_54.

Xiao, J., Zhang, J., Adler, B., Zhang, H., Zhang, J.: Three-dimensional point cloud plane segmentation in both structured and unstructured environments. Robotics and Autonomous Systems. 61(12), 1641-1652 (2013) DOI: 10.1016/j.robot.2013.07.001.

Durrant-White, H., Bailey T.: Simultaneous localization and mapping (SLAM): Part I. IEEE Robotics and Automation Magazine 13(2), 99-108. DOI: 10.1109/MRA.2006.1638022.

Bailey, T., Durrant-White, H.: Simultaneous localization and mapping (SLAM): Part II. IEEE Robotics and Automation Magazine 13(3), 108-117. DOI: 10.1109/MRA.2006.1678144.