Comparison of Two Sensor Models for Multi-camera Rig System in Measurements

  • JI Shunping ,
  • SHI Yun
Expand
  • 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430049, China;
    2. Key Laboratory of Agri-informatics Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
    3. CSIS, the University of Tokyo, Tokyo, Japan

Received date: 2013-05-20

  Revised date: 2014-04-18

  Online published: 2014-12-23

Abstract

According to a rigorous sensor model for multi-camera rig system, the error sources of the most widely used ideal panoramic sensor model are indicated, error distribution laws are deduced, and comprehensive comparison between the two models are given. First, the resection and 3D localization errors of the ideal model are analyzed respectively in a single panoramic image that shows the correlations both with the object-image distance and the viewing angle. Second, the epipolar errors of the stereo-pairs are analyzed, which are both affected by the rotation angles and z-coordinates of the image points. Finally, the comparative experiments are carried out in space resection, epipolar constraints and bundle adjustment with the two sensor models. The outdoor test shows the difference between the two models is slight, and both models achieve 1 pixel accuracy. In contrast, the indoor test shows that the rigorous model is stricter and produces obviously better measurement accuracy than the ideal model. All the test results are consistent with the deduced analytical error laws of the ideal panoramic sensor model.

Cite this article

JI Shunping , SHI Yun . Comparison of Two Sensor Models for Multi-camera Rig System in Measurements[J]. Acta Geodaetica et Cartographica Sinica, 2014 , 43(12) : 1252 -1258 . DOI: 10.13485/j.cnki.11-2089.2014.0169

References

[1] ANGUELOV D, DULONG C, FILIP D, et al. Google Street View: Capturing the World at Street Level [J]. Computer, 2010, 43(6): 32-38.
[2] XIAO Xiao. High Resolution Panoramic Imaging System and Visual Application [D]. Hangzhou:Zhejiang University, 2009. (肖潇. 高分辨率全景成像系统及其视觉应用研究[D].杭州: 浙江大学, 2009.)
[3] YANG Shaoping, CHEN Xiong, KONG Qingsheng. Image-based Visual Serving for Mobile Robots with Central Panoramic Camera [J]. Computers Engineering and Design, 2010, 31(19):4261-4264. (杨少平,陈 雄,孔庆生. 采用全景相机的移动机器人视觉伺服[J]. 计算机工程与设计, 2010, 31(19):4261-4264.)
[4] IKEDA S, SATO T, YOKOYA N. High-resolution Panoramic Movie Generation from Video Streams Acquired by An Omnidirectional Multi-camera System [C]// Proceedings of IEEE 19 International Conference on Multisensor Fusion and Integration for Intelligent Systems(MFI2003).[S.l.]:IEEE,2003:155-160.
[5] MEI C, BENHIMANE S, MALIS E, et al. Efficient Homography-based Tracking and 3-D Reconstruction for Single-viewpoint Sensors [J]. IEEE Transactions on Robotics, 2008, 24(6): 1352-1364.
[6] GEYER C, DANIILIDIS K. Catadioptric Projective Geometry [J]. International Journal of Computer Vision, 2001, 45(3): 223-243.
[7] BARRETO P, ARAUJO H. Geometric Properties of Central Catadioptric Line Images and Their Application in Calibration [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(1): 1327-1333.
[8] PARIAN A, GRUEN A. Sensor Modeling, Self-calibration and Accuracy Testing of Panoramic Cameras and Laser Scanners [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65: 60-76.
[9] SCHNEIDER D, MAAS H. A Geometric Model for Linear-array-based Terrestrial Panoramic Cameras [J]. Photogrammetric Record, 2006, 21(115): 198-210.
[10] KAESS M, DELLAERT F. Probabilistic Structure Matching for Visual SLAM with A Multi-camera Rig [J]. Computer Vision and Image Understanding, 2010, 114(2): 286-296.
[11] PAYA L, FERNANDEZ L, GIL A, et al. Map Building and Monte Carlo Localization Using Global Appearance of Omnidirectional Images [J]. Sensors, 2010, 10(12): 11468-11497.
[12] GUTIERREZ D, RITUERTO A, MONTIEL J, et al. Adapting A Real-time Monocular Visual SLAM from Conventional to Omnidirectional Cameras [C]// Proceedings of the 11th OMNIVIS in IEEE International Conference on Computer Vision (ICCV).Barcelona:IEEE,2011:343-350.
[13] SILPA ANAN C, HARTLEY R. Visual Localization and Loop-back Detection with A High Resolution Omni-directional Camera [C]//Workshop on Omnidirectional Vision and Camera Networks. [S.l.]: Omnivis,2005.
[14] TARDIF J, PAVLIDIS Y, DANIILIDIS K. Monocular Visual Odometry in Urban Environments Using An Omnidirectional Camera [C]//Proceedings of IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).[S.l.]:IEEE,2008:2531-2538.
[15] JI Shunping, SHI Yun. Image Matching and Bundle Adjustment Using Vehicle-based Panoramic Camera [J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 94-100. (季顺平, 史云. 车载全景相机的影像匹配和光束法平差[J]. 测绘学报, 2013, 42(1): 94-100.)
[16] SHI Yun, JI Shunping, SHI Zhongchao, et al. GPS-supported Visual SLAM with A Rigorous Sensor Model for A Panoramic Camera in Outdoor Environments [J]. Sensors, 2013, 13(1): 119-136.
[17] KANNALA J, BRANDT S. A Generic Camera Model and Calibration Method for Conventional, Wide-angle, and Fish-eye Lenses [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(8): 1335-1340.
[18] SATO T, IKEDA S, YOKOYA N. Extrinsic Camera Parameter Recovery from Multiple Image Sequences Captured by An Omni-directional Multi-camera System [J]. Camera, 2004, 2: 326-340.
[19] SATO T, YOKOYA N. Efficient Hundreds-baseline Stereo by Counting Interest Points for Moving Omni-directional Multi-camera System [J]. Journal of Visual Communication and Image Representation, 2010, 21(5-6):416-426.
[20] LI Deren, ZHENG Zhaobao. Analytical Photogrammetry [M]. Beijing:Suveying and Mapping Publications, 1992. (李德仁, 郑肇葆. 解析摄影测量学[M]. 北京:测绘出版社, 1992.)
[21] KANG Zhizhong, ZHANG Zuxun, YANG Fanlin. Relative Orientation and Epipolar Arrangement Based on Forward Moving Image Pairs along the Optical Axis [J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(1): 56-61. (康志忠, 张祖勋, 阳凡林. 基于沿主光轴方向摄影立体像对的相对定向与核线排列[J]. 测绘学报, 2007, 36(1): 56-61.)
Outlines

/