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Depth-Camera Calibration Optimization Method Based on Homography Matrix

Published: 20 September 2019 Publication History

Abstract

A self-calibration method of the depth-camera based on homography matrix is proposed to improve the accuracy and ease of use in calibrating the depth-camera. Firstly, the shortcomings of the existing self-calibration methods are pointed out, and the internal conversion model of the depth-camera is established. Then, the proposed self-calibration mathematical model of the depth-camera is deduced according to the imaging relationship of image pairs, and the internal and external parameters of the depth-camera are obtained by solving the essential matrix and the corresponding matrix. Finally, the distortion parameters of the depth-camera are obtained by an immune clonal selection algorithm being used for optimizing the distortion model of the depth image. Compared with Zhang's calibration method, the relative errors of the focal length and the coordinates obtained by the proposed self-calibration calibration method are smaller, in addition it has a good restoration effect for the edge distortions of the depth images.

References

[1]
David F. Fouhey, Alvaro Collet, Martial Hebert, et al. (2012). Object Recognition Robust to Imperfect Depth Data. European Conference on Computer Vision, 83--92.
[2]
Abdalmageed W, Wu Y, Rawls S, et al. (2016). Face Recognition Using Deep Multi-Pose Representations. IEEE Winter Conference on Applications of Computer Vision (WACV).
[3]
Henry P, Krainin M, Herbst E, et al. (2014). RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments. Experimental Robotics, 477--491.
[4]
Endres F, Jürgen Hess, Engelhard N, et al. (2012). An evaluation of the RGB-D SLAM system. IEEE International Conference on Robotics and Automation.
[5]
Do M Q, Lin C H. (2015). Embedded human-following mobile-robot with an RGB-D camera. Iapr International Conference on Machine Vision Applications.
[6]
Lima, J. P. A. B., Teixeira, J. M. A. B., Teichrieb, V. B. (2014). RGB-D based detection of texture-less planar objects applied to an AR jigsaw puzzle. Virtual & Augmented Reality. IEEE.
[7]
Ao L, Liu Y, Dong X, et al. (2015). A novel extrinsic calibration method of ToF cameras based on a virtual multi-cubes shaped object. Applied Advanced Optical Metrology Solutions. International Society for Optics and Photonics.
[8]
Chen G, Cui G, Jin Z, et al. (2018). Accurate Intrinsic and Extrinsic Calibration of RGB-D Cameras with GP-based Depth Correction. IEEE Sensors Journal, 1--1.
[9]
Staranowicz A N, Brown G R, Morbidi F, et al. (2015). Practical and accurate calibration of RGB-D cameras using spheres. Computer Vision and Image Understanding, 137:102--114.
[10]
Po-Chang S, Ju S, Wanxin X, et al. (2018). A Fast and Robust Extrinsic Calibration for RGB-D Camera Networks. Sensors, 18(1):235-.
[11]
Faugeras O D, Luong Q T, Maybank S J. (1992). Camera self-calibration: Theory and experiments. European Conference on Computer Vision.
[12]
Meng X Q, Hu Z Y. (2002). Research and development of camera self-calibration method. Acta Automatica Sinica, 29(1): 110--124.
[13]
Hartley R I, Hartley I R. (1992). Estimation of relative camera positions for uncalibrated cameras[C]// European Conference on Computer Vision. Springer-Verlag.
[14]
Hartley R. (1994). Euclidean reconstruction and invariants from multiple images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(10): 1036--1041.
[15]
Faugeras O D, Luong Q T, Maybank S J. (1998). Camera Self-Calibration: Theory and Experiments. European Conference on Computer Vision. Springer-Verlag.
[16]
Maybank S J, Faugeras O D. (1992). A theory of self-calibration of a moving camera. International Journal of Computer Vision, 8(2):123--151.
[17]
Triggs B. (1997). Autocalibration and the Absolute Quadric. Conference on Computer Vision & Pattern Recognition. IEEE Computer Society.
[18]
Khoshelham K. (2011). Accuracy analysis of kinect depth data. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3812(5):133--138.
[19]
Richard I, Hartley. (1997). In Defense of the Eight-Point Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence. 19(06): 104--913.
[20]
Boudine B, Kramm S, Akkad N E, et al. (2016). A flexible technique based on fundamental matrix for camera self-calibration with variable intrinsic parameters from two views. Journal of Visual Communication & Image Representation, 39(C): S104732031630061X.
[21]
Huang T S, Faugeras O D. (1989). Some properties of the E matrix in two-view motion estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 111(12):0--1312.
[22]
Nister D. (2004). An Efficient Solution to the Five Point Relative Pose Problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6): 756--770.
[23]
Rublee E, Rabaud V, Konolige K, et al. (2011). ORB: An efficient alternative to SIFT or SURF[C]// International Conference on Computer Vision, 2564--2571.
[24]
Zhang Z. (2000). A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330--1334.
[25]
Shi G, Ma J.(Ed.) 2014. Research and application of immune clonal selection algorithm. Northeastern University Press, Shenyang.

Cited By

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  • (2023)A Review of Homography Estimation: Advances and ChallengesElectronics10.3390/electronics1224497712:24(4977)Online publication date: 12-Dec-2023
  • (2022)Multi-View Auto-Calibration Method Based on Human Pose Estimation2022 4th International Conference on Robotics and Computer Vision (ICRCV)10.1109/ICRCV55858.2022.9953175(75-79)Online publication date: 25-Sep-2022
  • (2021)Many-Objective Deployment Optimization for a Drone-Assisted Camera NetworkIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30579158:4(2756-2764)Online publication date: 1-Oct-2021

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cover image ACM Other conferences
RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence
September 2019
803 pages
ISBN:9781450372985
DOI:10.1145/3366194
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2019

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Author Tags

  1. Calibration
  2. Depth image
  3. Depth-Camera
  4. Homography matrix
  5. Optimization

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RICAI 2019

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RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;
Overall Acceptance Rate 140 of 294 submissions, 48%

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Cited By

View all
  • (2023)A Review of Homography Estimation: Advances and ChallengesElectronics10.3390/electronics1224497712:24(4977)Online publication date: 12-Dec-2023
  • (2022)Multi-View Auto-Calibration Method Based on Human Pose Estimation2022 4th International Conference on Robotics and Computer Vision (ICRCV)10.1109/ICRCV55858.2022.9953175(75-79)Online publication date: 25-Sep-2022
  • (2021)Many-Objective Deployment Optimization for a Drone-Assisted Camera NetworkIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30579158:4(2756-2764)Online publication date: 1-Oct-2021

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