隧道掘进机（Tunnel boring machine，TBM）被广泛地运用于隧道施工中。隧道的变形是影响TBM施工安全的主要因素之一，过大的隧道变形会引起隧道塌方、TBM卡机等事故，影响施工进度，威胁施工人员的生命安全。针对TBM施工情况，介绍基于视觉辅助的隧道轮廓监测系统。采用荧光涂料设计人工标志点，单目视觉检测目标，云台驱动激光位移传感器瞄准目标。基于极坐标测量原理，获取目标点三维坐标。为进一步提高测量效率，提出基于半定规划的双目视觉系统辅助测量方案。试验结果表明系统的精度可达1 mm并具有较高的测量效率。
Tunnel boring machine (TBM) has been widely applied in tunnel construction. The deformation of tunnel is one of the major concerns in TBM construction project. Major deformation may cause tunnel landslide or TBM jam, influence the construction schedule and threaten the safety of the constructors. A tunnel deformation monitoring system based on vision assistant in TBM construction is proposed. Artificial target points are designed by using fluorescent coating. Targets are detected by using monocular vision and aimed by using a Pan-tilt platform. The three-dimensional coordinates of the targets are obtained based on the principal of polar coordinates of the Pan-tilt platform using laser distance measurement. The efficiency of measuring is further improved by using a binocular vision system assistant measurement based on semi-definite programming method. Experiments show the precision of the system can be up to 1mm with good efficiency.
[1] 杜彦良,杜立杰. 全断面岩石隧道掘进机系统原理与集成设计[M]. 武汉:华中科技大学出版社,2011. DU Yanliang,DU Lijie. Full face hard rock boring machine-system principles and integrated design[M]. Wuhan:Huazhong University of Science and Technology Press,2011.
[2] 苏华友,任月宗,薛继洪. 不良地质条件对TBM施工的影响与探讨[J]. 中国钨业,2009,24(2):44-47. SU Huayou,REN Yueming,XUE Jihong. Adverse geological conditions effect and analysis on tunnel boring machine construction[J]. China Tungsten Industry,2009,24(2):44-47.
[3] BASSETT R H,KIMMANCE J P,RASMUSSEN C. An automated electrolevel deformation monitoring system for tunnels[J]. Proceedings of the ICE-Geotechnical Engineering,1999,137(3):117-125.
[4] NARUSE H,KOMATSU K,FUJIHASHI K,et al. Telecommunications tunnel monitoring system based on distributed optical fiber strain measurement[J]. Proc. SPIE,2005,5855:168-171.
[5] STURM P. Computer vision[M]. Boston:Springer,2014.
[6] ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2000,22(11):1330-1334.
[7] 刘媛媛,赵冬娥. 对于斜入射滤光片的分析[J]. 电子世界,2013(15):28-29. LIU Yuanyuan,ZHAO Donge. Analysis of oblique incidence filter[J]. Electronics World,2013(15):28-29.
[8] 张铮,王艳平,薛桂香. 数字图像处理与机器视觉[M]. 北京:人民邮电出版社,2010. ZHANG Zhen,WANG Yanping,XUE Guixiang. Digital image processing and machine vision[M]. Beijing:Posts and Telecom Press,2010.
[9] CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-98.
[10] CORKE P I,SPINDLER F,CHAUMETTE F. Combining Cartesian and polar coordinates in IBVS[C]//The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. October 11-15,2009,St. Louis,USA. New York:IEEE Press,2009:5962-5967.
[11] KIM S,KOJIMA M,WAKI H. Exploiting sparsity in SDP relaxation for sensor network localization[J]. SIAM Journal on Optimization,2009,20(1):192-215.
[12] BISWAS P,YE Y. Semidefinite programming for ad hoc wireless sensor network localization[C]//International Symposium on Information Processing in Sensor Networks. April 26-27,2004,Berkeley,California,USA. New York:ACM,2004:46-54.
[13] STURM J F. Using SeDuMi 1.02,a MATLAB toolbox for optimization over symmetric constraints[J]. Optimization Methods and Software,1998,11(1-4):625-653.
