一种基于全向结构光的深度测量方法

贾同; 吴成东; 陈东岳; 王炳楠; 高海红; 房卓群

doi:10.16383/j.aas.2015.c140857

一种基于全向结构光的深度测量方法

doi: 10.16383/j.aas.2015.c140857

1.
东北大学信息科学与工程学院沈阳 110819

基金项目:

国家自然科学基金(61273078),教育部博士点基金(20110042120030),中央高校基础科研业务费(130404012)资助

详细信息

作者简介:
吴成东博士,东北大学信息科学与工程学院教授.主要研究方向为图像处理,无线传感器网络,建筑智能化技术,机器人控制.E-mail:wuchengdong@ise.neu.edu.cn

陈东岳博士,东北大学信息科学与工程学院副教授.主要研究方向为图像处理,计算机视觉,模式识别.E-mail:chendongyue@ise.neu.edu.cn

王炳楠沈阳建筑大学硕士研究生.主要研究方向为图像处理与计算机视觉.E-mail:wangbingnan@163.com

高海红东北大学硕士研究生.主要研究方向为图像处理与计算机视觉.E-mail:dzgaohaihong@163.com

房卓群东北大学博士研究生.主要研究方向为图像处理与计算机视觉.E-mail:fangzhuoqun@163.com

通讯作者:
贾同博士,东北大学信息科学与工程学院副教授.主要研究方向为图像处理,计算机视觉,模式识别.本文通信作者.E-mail:jiatong@ise.neu.edu.cn

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出版历程
- 收稿日期: 2014-12-09
- 修回日期: 2015-05-28
- 刊出日期: 2015-09-20

A Depth Measurement Method by Omni Directional Image and Structured Light

1.
College of Information Science and Engineering, Northeastern University, Shenyang 110819

Funds:

Supported by National Natural Science Foundation of China (61273078), Doctoral Foundation of Ministry of Education of China (20110042120030), and Fundamental Research Funds for the Central Universities of China (130404012)

摘要

摘要: 深度测量是立体视觉研究的重要问题, 本文提出一种基于全向图与结构光的深度测量方法.首先,根据测量系统特点,采用了基于多参考面的投影仪标定算法;然后,设计了一组 "四方位沙漏状"编码结构光,实现待测图像与参考图像的对应点计算;最后,在移动条件下,研究基于先验约束迭代就近点(Iterative closest point, ICP)的深度点云匹配算法. 实验结果表明,本文方法可以准确地对室内场景进行深度测量,且抗干扰能力较强.
- 深度测量 /
- 全向图 /
- 结构光 /
- 立体视觉
Abstract: Depth measurement is an important problem in stereovision. A depth measurement method based on omnidirectional image and structured light is proposed. Firstly, according to the measurement system characteristics, main research attentions are paid to the study of projector calibration algorithm based on multiple reference planes. Secondly, a group of "four direction sand clock-like" encoding structured light is designed. It can be used to compute the corresponding points between the measured image and reference image. Thirdly, under a mobile condition, a depth point-cloud matching algorithm based on prior-constraint iterative closest point (ICP) is studied. Experimental results demonstrate that the proposed method can acquire omnidirectional depth information of large-scale scenes accurately, and has high anti-interference abilility.
- Depth measurement /
- omnidirectional image /
- structured light /
- stereovision

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参考文献(35)

[1]	Zhang H, Reardon C, Parker L E. Real-time multiple human perception with color-depth cameras on a mobile robot. IEEE Transactions on Cybernetics, 2013, 43(5): 1429-1441
[2]	Tai Y C, Gowrisankaran S, Yang S N, Sheedy J E, Hayes J R, Younkin A C, Corriveau P J. Depth perception from stationary and moving stereoscopic three-dimensional images. In: Proceedings of the SPIE---The International Society for Optical Engineering. Burlingame, California, USA: SPIE, 2013. 8648-8658
[3]	Xu Yu-Hua, Tian Zun-Hua, Zhang Yue-Qiang, Zhu Xian-Wei, Zhang Xiao-Hu. Adaptively combining color and depth for human body contour tracking. Acta Automatica Sinica, 2014, 40(8): 1623-1634(徐玉华, 田尊华, 张跃强, 朱宪伟, 张小虎. 自适应融合颜色和深度信息的人体轮廓跟踪. 自动化学报, 2014, bf 40(8): 1623-1634)
[4]	Jung B, Sukhatme G S. Real-time motion tracking from a mobile robot. International Journal of Social Robotics, 2010, 2(1): 63-78
[5]	Guo Chun-Zhao, Yamabe Takayuki, Mita Seiichi. Drivable road boundary detection for intelligent vehicles based on stereovision with plane-induced homography. Acta Automatica Sinica, 2013, 39(4): 371-380(郭春钊, 山部尚孝, 三田诚一. 基于立体视觉平面单应性的智能车辆可行驶道路边界检测. 自动化学报, 2013, 39(4): 371-380)
[6]	Wei B Y, Gao J Y, Li K J, Fan Y, Gao X S, Gao B Q. Indoor mobile robot obstacle detection based on linear structured light vision system. In: Proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics. Bangkok, Thailand: IEEE, 2009. 834-839
[7]	Susperregi L, Sierra B, Castrillón M, Lorenzo J, Martínez-Otzeta J M, Lazkano E. On the use of a low-cost thermal sensor to improve Kinect people detection in a mobile robot. Sensors, 2013, 13(11): 14687-14713
[8]	Luo Bin, Wang Yong-Tian, Shen Hao, Wu Zhi-Jie, Liu Yue. Overview of hybrid tracking in augmented reality. Acta Automatica Sinica, 2013, 39(8): 1185-1201(罗斌, 王涌天, 沈浩, 吴志杰, 刘越. 增强现实混合跟踪技术综述. 自动化学报, 2013, 39(8): 1185-1201)
[9]	Firoozfam P, Negahdaripour S. A multi-camera conical imaging system for robust 3D motion estimation, positioning and mapping from UAVs. In: Proceedings of the 2003 IEEE Conference on Advanced Video and Signal Based Surveillance. Miami, FL, USA: IEEE, 2003. 99-106
[10]	Yi S, Choi B, Ahuja N. Real-time omni-directional distance measurement with active panoramic vision. International Journal of Control, Automation, and Systems, 2007, 5(2): 184-191
[11]	Yi S, Suh J, Hong Y, Hwang D. Active ranging system based on structured laser light image. In: Proceedings of SICE Annual Conference. Taipei, China: IEEE, 2010. 747-752
[12]	Matsui K, Yamashita A, Kaneko T. 3-D shape measurement of pipe by range finder constructed with omni-directional laser and omni-directional camera. In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA). Anchorage, Alaska: IEEE, 2010. 2537-2542
[13]	Orghidan R, Salvi J, Mouaddib E M. Modelling and accuracy estimation of a new omnidirectional depth computation sensor. Pattern Recognition Letters, 2006, 27(7): 843-853
[14]	Zhou F Q, Peng B, Cui Y, Wang Y X, Tan H S. A novel laser vision sensor for omnidirectional 3D measurement. Optics & Laser Technology, 2013, 45(2): 1-12
[15]	Tang Yi-Ping, Wu Li-Juan, Zhou Jing-Kai. 3D active stereo omni-directional vision sensing technology. Chinese Journal of Computers, 2014, 37(6): 1289-1300(汤一平, 吴立娟, 周静恺. 主动式三维立体全景视觉传感技术. 计算机学报, 2014, 37(6): 1289-1300)
[16]	Jia T, Shi Y, Zhou Z X, Chen D Y. 3D depth information extraction with omni-directional camera. Information Processing Letters, 2015, 115(2): 285-291
[17]	Xu J, Liu S L, Wan A, Gao B T, Yi Q, Zhao D P, Luo R K, Chen, K. An absolute phase technique for 3D profile measurement using four-step structured light pattern. Optics and Lasers in Engineering, 2012, 50(9): 1274-1280
[18]	Yang W Z, Zhang G F, Bao H J, Kim J, Lee H Y. Consistent depth maps recovery from a trinocular video sequence. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE, 2012. 1466-1473
[19]	Zhang J, Kan C, Schwing A G, Urtasun R. Estimating the 3D layout of indoor scenes and its clutter from depth sensors. In: Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE, 2013. 1273-1280
[20]	Chen Wang, Zhang Mao-Jun, Xiong Zhi-Hui, Lou Jing-Tao. Horizontal line 3D reconstruction from two image points based on single Catadioptric omni-directional image. Journal of Image and Graphics, 2010, 15(12): 1796-1803(陈旺, 张茂军, 熊志辉, 娄静涛. 基于单幅折反射全向图的水平直线3维重建. 中国图象图形学报, 2010, 15(12): 1796-1803)
[21]	Toda Y, Kubota N. Self-Localization based on multiresolution map for remote control of multiple mobile robots. IEEE Transactions on Industrial Informatics, 2013, 9(3): 1772-1781
[22]	Lee S O, Lim H, Kim H G, Ahn S C. RGB-D fusion: Real-time robust tracking and dense mapping with RGB-D data fusion. In: Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). Chicago, IL, USA: IEEE, 2014. 2749-2754
[23]	Li Zhong-Wei, Shi Yu-Sheng, Zhong Kai, Wang Cong-Jun. Projector calibration algorithm for the structured light measurement technique. Acta Optica Sinica, 2009, 29(11): 3061-3065(李中伟, 史玉升, 钟凯, 王从军. 结构光测量技术中的投影仪标定算法. 光学学报, 2009, 29(11): 3061-3065)
[24]	Dai Xiao-Lin, Zhong Yue-Xian, Yuan Chao-Long, Ma Yang-Biao. Research on projector calibration in one-camera 3-D measurement systems. Machinery Design & Manufacture, 2008, (8): 194-196(戴小林, 钟约先, 袁朝龙, 马扬飚. 单摄像机结构光扫描系统中投影仪标定技术. 机械设计与制造, 2008, (8): 194-196)
[25]	Luo H F, Gao B T, Xu J, Chen K. An approach for structured light system calibration. In: Proceedings of the 2013 IEEE 3rd Annual International Conference on Cyber Technology in Automation, Control and Intelligent Systems (CYBER). Nanjing, China: IEEE, 2013. 428-433
[26]	Chen X B, Xi J T, Jin Y, Sun J. Accurate calibration for a camera-projector measurement system based on structured light projection. Optics and Lasers in Engineering, 2009, 47(3-4): 310-319
[27]	Salvi J, Pagés J, Batlle J. Pattern codification strategies in structured light systems. Pattern Recognition, 2004, 37(4): 827-849
[28]	Zhang S, Yau S T. High-speed three-dimensional shape measurement system using a modified two-plus-one phase-shifting algorithm. Optical Engineering, 2007, 46(11): 113603
[29]	Kim D, Ryu M, Lee S. Antipodal gray codes for structured light. In: Proceedings of the 2008 IEEE International Conference on Robotics and Automation. Pasadena, CA, USA: IEEE, 2008. 3016-3021
[30]	Tehrani M A, Saghaeian A, Mohajerani O R. A new approach to 3D modeling using structured light pattern. In: Proceedings of 3rd International Conference on Information and Communication Technologies: From Theory to Applications. Damascus: IEEE, 2008. 1-5
[31]	Cheng F H, Lu C T, Huang Y S. 3D Object scanning system by coded structured light. In: Proceedings of the 3rd International Symposium on Electronic Commerce and Security. Guangzhou: IEEE, 2010. 213-217
[32]	Fechteler P, Eisert P. Adaptive colour classification for structured light systems. IET Computer Vision, 2009, 3(2): 49-59
[33]	Pagés J, Salvi J, Collewet C, Forest J. Optimised de Bruijn patterns for one-shot shape acquisition. Image and Vision Computing, 2005, 23(8): 707-720
[34]	Yu Qing-Cang. A Study on Symbol M-Array Based 3D Detection Approach Utilizing Binary Structured Light [Ph.D. dissertation], Zhejiang University, China, 2007(喻擎苍. 基于符号M阵列二值结构光的三维检测方法的研究 [博士学位论文], 浙江大学, 中国, 2007)
[35]	Jia T, Zhou Z X, Gao H H. Depth measurement based on infrared coded structured light. Journal of Sensors, 2014, 2014: Article ID 852621