Inferring Bias and Uncertainty in Camera Calibration (original) (raw)

References

• Abraham, S., & Förstner, W. (1998). Calibration errors in structure from motion. In P. Levi, M. Schanz, R. J. Ahlers, & F. May (Eds.), Mustererkennung 1998 (pp. 117–124). Berlin Heidelberg: Springer.
  Chapter Google Scholar
• Beck, J., & Stiller, C. (2018). Generalized B-spline Camera Model. In: 2018 IEEE Intelligent Vehicles Symposium (IV). pp. 2137–2142. IEEE, https://ieeexplore.ieee.org/abstract/document/8500466/
• Bodenham, D. A., & Adams, N. M. (2016). A comparison of efficient approximations for a weighted sum of chi-squared random variables. Statistics and Computing, 26(4), 917–928.
  Article MathSciNet Google Scholar
• Cheong, L. F., & Peh, C. H. (2004). Depth distortion under calibration uncertainty. Computer Vision and Image Understanding, 93(3), 221–244. https://doi.org/10.1016/j.cviu.2003.09.003
  Article Google Scholar
• Cheong, L. F., & Xiang, X. (2011). Behaviour of SFM algorithms with erroneous calibration. Computer Vision and Image Understanding, 115(1), 16–30. https://doi.org/10.1016/j.cviu.2010.08.004
  Article Google Scholar
• Cramariuc, A., Petrov, A., Suri, R., Mittal, M., Siegwart, R., & Cadena, C. (2020). Learning camera miscalibration detection. arXiv preprint arXiv:2005.11711
• Davison, A.C., & Hinkley, D.V. (1997). Bootstrap methods and their application. No. 1, Cambridge university press
• Doran, H. E., Doran, H., et al. (1989). Applied regression analysis in econometrics (Vol. 102). CRC Press.
• Edwards, M.J., Hayes, M.P., & Green, R.D. (2020)Experimental validation of bias in checkerboard corner detection. In: 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). pp. 1–6. IEEE
• Förstner, W., & Wrobel, B. P. (2016). Photogrammetric computer vision. Berlin: Springer.
  Book Google Scholar
• Furgale, P., Rehder, J., Siegwart, R. (2013). Unified temporal and spatial calibration for multi-sensor systems. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 1280–1286. IEEE
• Hagemann, A., Knorr, M., Janssen, H., Stiller, C. (2020). Bias detection and prediction of mapping errors in camera calibration. In: German Conference on Pattern Recognition. Springer , in press.
• Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision, https://doi.org/10.1017/CBO9780511811685, oCLC: 171123855
• Imhof, J. P. (1961). Computing the distribution of quadratic forms in normal variables. Biometrika, 48(3/4), 419–426.
  Article MathSciNet Google Scholar
• Lavest, J.M., Viala, M., Dhome, M. (1998). Do we really need an accurate calibration pattern to achieve a reliable camera calibration? In: European Conference on Computer Vision. pp. 158–174. Springer
• Luhmann, T., Robson, S., Kyle, S., & Boehm, J. (2013). Close-range photogrammetry and 3D imaging. Beijing: Walter de Gruyter.
  Book Google Scholar
• MathWorks: Matlab Computer Vison Toolbox, https://de.mathworks.com/help/vision/camera-calibration-and-3-d-vision.html, retrieved on January 8th, 2021
• Mei, C., Rives, P.: Single view point omnidirectional camera calibration from planar grids. In: Proceedings 2007 IEEE International Conference on Robotics and Automation. pp. 3945–3950. IEEE (2007)
• OpenCV: OpenCV Fisheye Camera Model, https://docs.opencv.org/master/db/d58/group_calib3d_fisheye.html, retrieved on January 8th, 2021
• OpenCV: OpenCV Tutorial Camera Calibrations, https://docs.opencv.org/master/d7/d21/tutorial_interactive_calibration.html, retrieved on January 8th, 2021
• Oth, L., Furgale, P., Kneip, L., Siegwart, R. (2013). Rolling shutter camera calibration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1360–1367
• Ozog, P., & Eustice, R.M. (2013). On the importance of modeling camera calibration uncertainty in visual SLAM. In: 2013 IEEE International Conference on Robotics and Automation. pp. 3777–3784. IEEE, https://ieeexplore.ieee.org/abstract/document/6631108/
• Peng, S., & Sturm, P .(2019). Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1497–1505
• Rautenberg, U., & Wiggenhagen, M. (2002). Abnahme und ueberwachung photogrammetrischer messsysteme nach vdi 2634, blatt 1. PFG 2/2002, S.117-124 , https://www.ipi.uni-hannover.de/fileadmin/ipi/publications/VDI2634_1e.pdf
• Richardson, A., Strom, J., & Olson, E. (2013). AprilCal: Assisted and repeatable camera calibration. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 1814–1821. IEEE, https://ieeexplore.ieee.org/abstract/document/6696595/
• Rojtberg, P., Kuijper, A. (2018). Efficient pose selection for interactive camera calibration. In: 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). pp. 31–36. IEEE , https://ieeexplore.ieee.org/abstract/document/8613748/
• ROS: ROS Tutorial MonocularCalibration, http://wiki.ros.org/camera_calibration/Tutorials/MonocularCalibration, retrieved on January 8th, 2021
• Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the median absolute deviation. Journal of the American Statistical association, 88(424), 1273–1283.
  Article MathSciNet Google Scholar
• Scaramuzza, D., Martinelli, A., & Siegwart, R. (2006). A toolbox for easily calibrating omnidirectional cameras. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 5695–5701. IEEE
• Schoeps, T., Larsson, V., Pollefeys, M., & Sattler, T. (2019). Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve. arXiv preprint arXiv:1912.02908
• Semeniuta, O. (2016). Analysis of camera calibration with respect to measurement accuracy. Procedia Cirp, 41, 765–770.
  Article Google Scholar
• Strauss, T. (2015). Kalibrierung von Multi-Kamera-Systemen. KIT Scientific Publishing, https://d-nb.info/1082294497/34
• Strauß, T., Ziegler, J., & Beck, J. (2014). Calibrating multiple cameras with non-overlapping views using coded checkerboard targets. In: 17th international IEEE conference on intelligent transportation systems (ITSC). pp. 2623–2628. IEEE
• Sturm, P.F., & Maybank, S.J. (1999). On plane-based camera calibration: A general algorithm, singularities, applications. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149). vol. 1, pp. 432–437. IEEE
• Sun, W., & Cooperstock, J. R. (2006). An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques. Machine Vision and Applications, 17(1), 51–67.
  Article Google Scholar
• Svoboda, T., & Sturm, P. (1996) What can be done with a badly calibrated Camera in Ego-Motion Estimation?, https://hal.inria.fr/inria-00525701/
• Triggs, B., McLauchlan, P.F., Hartley, R.I., & Fitzgibbon, A.W. (2000). Bundle Adjustment - A Modern Synthesis. In: Goos, G., Hartmanis, J., van Leeuwen, J., Triggs, B., Zisserman, A., Szeliski, R. (eds.) Vision Algorithms: Theory and Practice, vol. 1883, pp. 298–372. Springer Berlin Heidelberg, Berlin, Heidelberg, https://doi.org/10.1007/3-540-44480-7_21
• Vision, A. (2021). Manta g-235, https://www.alliedvision.com/en/products/cameras/detail/Manta/G-235.html, retrieved on January 11th,
• Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334.
  Article Google Scholar
• Zucchelli, M., & Kosecka, J. (2001). Motion bias and structure distortion induced by calibration errors. pp. 68.1–68.10. British Machine Vision Association https://doi.org/10.5244/C.15.68, http://www.bmva.org/bmvc/2001/papers/51/index.html

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