遥控焊接机器人任务空间的三维重建研究
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摘要
核电站的维修、空间结构的建造以及海洋工程的建设大量用到了焊接技术,机器人遥控焊接技术是代替人进入这类危险、极限环境中执行焊接操作的最佳选择。在非结构化环境的遥控焊接中,通过机器人任务空间的三维重建,发挥机器人自动化功能可以避免对操作者的过分依赖、提高系统的效率和安全性。此外,焊接机器人全自主焊接的发展也需要提高机器人感知环境的能力。本文基于立体视觉技术,针对遥控焊接机器人任务空间的三维重建进行了深入研究,实现了无纹理焊接环境的三维建模。
本文建立了用于遥控焊接机器人任务空间三维重建的立体视觉系统,并完成了系统标定和极线校正。系统标定建立了机器人坐标系中空间点与摄像机图像上像素点的映射关系。极线校正算法通过对图像进行重采样,将对应极线变换到图像扫描线上,降低了立体匹配算法复杂度。
焊接场景中纹理的缺乏导致立体匹配尤为困难,本文采用主动光源向场景添加纹理,采用基于单图像对的特征匹配和基于图像序列的时空立体视觉算法完成立体匹配。
在单图像对的立体匹配中,使用角点特征和线特征两种特征。采用亚像素检测算法提取角点特征,以人机交互或者自动匹配算法完成了外围设备和简单工件图像对的立体匹配。本文提出一种焊缝线特征检测算法,用于焊缝图像的立体匹配。该算法通过底帽变换形态学预处理,突出焊缝区域,用Canny边缘检测和闭操作获得初始的像素级焊缝边缘点;依据焊缝的连续性和宽度有限对初始边缘点进行筛选,剔除错误点;然后用三次平滑样条拟合获得了光滑的亚像素精度的焊缝边缘点。在极线约束下完成焊缝特征的立体匹配。
本文设计了一种锯齿式多级对偶灰度光源序列向场景中添加纹理,并提出了归一化SSSD匹配算法,对时空立体视觉技术进行改进,解决了焊接无纹理环境的立体匹配问题。归一化SSSD匹配算法通过对不同时刻图像对SSD的归一化处理提高了匹配精度。采用曲线拟合的方法求解亚像素视差,通过左右一致性检验和图像序列的灰度跟踪,去除了视差图中的错误区域,并引入了各向异性扩散滤波对视差图进行保留深度突变的平滑处理。本文进一步对从视差图或者点云数据获得任务空间的三维模型进行了研究。根据视差图的特点可以直接从视差图生成场景的三角网格模型;另外,通过视差图的分割和曲面拟合也可获得焊接工件的标准几何模型。本文改进了USF距离图平面分割算法,增加了区域合并步骤,使其能够用于含有圆柱面的视差图的分割。对于分割后的属于不同表面的点云,通过曲面拟合的算法建立模型,这里对平面、一般二次曲面和圆柱面拟合进行了研究。其中,平面拟合采用特征向量分解法,一般二次曲面采用正则化最小二乘法拟合,而圆柱面拟合提出一种主成分分析法与非线性最小二乘迭代结合的新算法。针对焊缝,采用NURBS曲线拟合算法从焊缝点云数据建立了焊缝曲线模型。
针对典型的焊接任务空间,进行了外围设备、焊接工件和焊缝的三维重建实验,并对系统标定误差和重建结果误差进行了分析。标定的绝对误差在X、Y、Z方向上绝对值的平均数为[0.26 0.18 0.74]mm,最大误差的绝对值为[0.42 0.40 2.15]mm。外围设备顶点的绝对定位误差为6.53mm;在焊接工件的重建实验中,平面工件拟合误差为0.92mm,边长平均偏差3.43mm;马鞍形工件的拟合误差为1.35mm,高度和圆柱半径平均偏差2.25mm。焊缝的重建结果中,S型焊缝的重建焊缝与真实测量数据的平均距离误差为1.10mm,圆柱对接焊缝的平均距离误差为1.33mm。重建实验结果和误差实验结果显示,本文基于立体视觉的三维重建算法可以克服焊接场景无纹理的缺点,获得精度较高的重建结果,能够满足遥控机器人任务空间的建模需要。
Welding technology would be widely used in maintenance of nuclear plants, construction and repairing operations of underwater structures and outer space aircrafts. Telerobotic welding has become the best choice to perform welding tasks in those hazardous or extreme environments instead of human operator. When performing the weld task in unstructured environment, automatic robot functions can be employed by the recontruction of robot’s task space to avoid depending too much on human operator and to improve the efficiency and security. Furthermore, according to the development need of the completely autonomous welding, the ability to perceive envirionment of weld robot should be improved. The thesis investigates 3D reconstruction of the welding robot’s task space base on stereo vision, and 3D modeling of untextured weld environment is implemented.
Stereo vision system for 3D reconstruction of telerobotic’s task space is set up. The system is calibrated and epipolar rectification is applied to the image pair. By system calibration, the mapping between the point in the robot coordinate and the pixel in the image is built. Epipolar rectification resamples the image and transforms epipolar line to image scanline, so that the complex of stereo matching algorithm is reduced.
Lack of texture in weld scene makes stereo matching harder. Active illumination is applied to add artificial texture into the scene, and both feature matching of singe image pair and image sequence matching of spacetime stereo are investigated to accomplish stereo matching.
In single image pair matching, features of corners and lines are used. Subpixel corner detection algorithm is used to detect the corner features. Man-machine interaction and autonomously corner matching are used for image pairs of peripheral equipment and simple workpiece respectively. A new line feature detection algorithm is presented to accomplish stereo matching of weld seam. The image is preprocessed by bottom hat transformation of morphology to enhance weld seam section and initial closed pixel level edge points of the weld seam are obtained by Canny detector and close operation. According to the continuity and limited width of weld seam, these edge points are filtered to eliminate wrong data. Then the filtered data is fitted to cubic smooth spline and the subpixel weld edge features are obtained. These subpixel feature points of weld seam are matched by epipolar constraint.
A multi-level dual“saw-tooth”stripe gray light patterns are designed to add artificial texture into scene and a normalized SSSD matching algorithm is presented to revise spacetime stereo to accomplish the matching of images for the untextured weld environment. The normalized SSSD algorithm improves precision by normalizing the SSD of image pairs at different times. Subpixel disparity is calculated by fitting a second-degree curve to the SSSD values. By intensity tracking of the image sequence and left right consistant check, wrong regions are removed in the disparity map and anisotropy diffusion filtering is introduced to smooth disparity map while preserving depth discontinuity.
The problem of creating 3D model of task space from disparity map or point clouds is also studied. The scene’s triangulation mesh model can be directly created from disparity map. Besides, standard geometry model of weld workpiece can be obtained by disparity map segmentation and surface fitting. The USF range image plane segmentation algorithm is revised to segment disparity map containing cylinder surface via an additional step of region merging. The segmented point clouds, which belong to different surfaces, are then fitted to different surface models. The fitting algorithms for plane, general quadric and cylinder are investigated. Eigenvector method is used for plane fitting and regulation least squares method is used for quadric fitting. For cylinder fitting, a new algorithm of collaboration of principal component analysis and nonlinear least-squares algorithm is presented. For weld seam, NURBS curve fitting is used to create model from point cloud of weld seam
For typical weld task space, the reconstruction experiments for peripheral equipment, weld workpiece and weld seam are carried out. The errors of system calibration and reconstruction are analyzed. The mean of absolute value of the calibration’s absolute error along X、Y、Z axes are [0.26 0.18 0.74]mm, respectively. Mean absolute position error for corner of peripheral equipment is 6.53mm. The fitting error for plane workpiece is 0.92mm, and the mean error of the boundary is 3.43mm. For saddle-shape workpiece, the mean fitting error is 1.35mm, and mean error for cylinders’heights and radius is 2.25mm. In reconstruction results of weld seam, the mean distance of fitted curve to the measured true datas for S-weld seam is 1.10mm, and the mean distance for cylinder butt joint weld seam is 1.33mm. The reconstruction results and errors show that the 3D reconstruction algorithm based on stereo vision can overcome the problem of lack of texture in welding scene, relative high precision can be obtained, and the algorithm can satisfy the need of creating model of task space for telerobotic welding.
本文建立了用于遥控焊接机器人任务空间三维重建的立体视觉系统,并完成了系统标定和极线校正。系统标定建立了机器人坐标系中空间点与摄像机图像上像素点的映射关系。极线校正算法通过对图像进行重采样,将对应极线变换到图像扫描线上,降低了立体匹配算法复杂度。
焊接场景中纹理的缺乏导致立体匹配尤为困难,本文采用主动光源向场景添加纹理,采用基于单图像对的特征匹配和基于图像序列的时空立体视觉算法完成立体匹配。
在单图像对的立体匹配中,使用角点特征和线特征两种特征。采用亚像素检测算法提取角点特征,以人机交互或者自动匹配算法完成了外围设备和简单工件图像对的立体匹配。本文提出一种焊缝线特征检测算法,用于焊缝图像的立体匹配。该算法通过底帽变换形态学预处理,突出焊缝区域,用Canny边缘检测和闭操作获得初始的像素级焊缝边缘点;依据焊缝的连续性和宽度有限对初始边缘点进行筛选,剔除错误点;然后用三次平滑样条拟合获得了光滑的亚像素精度的焊缝边缘点。在极线约束下完成焊缝特征的立体匹配。
本文设计了一种锯齿式多级对偶灰度光源序列向场景中添加纹理,并提出了归一化SSSD匹配算法,对时空立体视觉技术进行改进,解决了焊接无纹理环境的立体匹配问题。归一化SSSD匹配算法通过对不同时刻图像对SSD的归一化处理提高了匹配精度。采用曲线拟合的方法求解亚像素视差,通过左右一致性检验和图像序列的灰度跟踪,去除了视差图中的错误区域,并引入了各向异性扩散滤波对视差图进行保留深度突变的平滑处理。本文进一步对从视差图或者点云数据获得任务空间的三维模型进行了研究。根据视差图的特点可以直接从视差图生成场景的三角网格模型;另外,通过视差图的分割和曲面拟合也可获得焊接工件的标准几何模型。本文改进了USF距离图平面分割算法,增加了区域合并步骤,使其能够用于含有圆柱面的视差图的分割。对于分割后的属于不同表面的点云,通过曲面拟合的算法建立模型,这里对平面、一般二次曲面和圆柱面拟合进行了研究。其中,平面拟合采用特征向量分解法,一般二次曲面采用正则化最小二乘法拟合,而圆柱面拟合提出一种主成分分析法与非线性最小二乘迭代结合的新算法。针对焊缝,采用NURBS曲线拟合算法从焊缝点云数据建立了焊缝曲线模型。
针对典型的焊接任务空间,进行了外围设备、焊接工件和焊缝的三维重建实验,并对系统标定误差和重建结果误差进行了分析。标定的绝对误差在X、Y、Z方向上绝对值的平均数为[0.26 0.18 0.74]mm,最大误差的绝对值为[0.42 0.40 2.15]mm。外围设备顶点的绝对定位误差为6.53mm;在焊接工件的重建实验中,平面工件拟合误差为0.92mm,边长平均偏差3.43mm;马鞍形工件的拟合误差为1.35mm,高度和圆柱半径平均偏差2.25mm。焊缝的重建结果中,S型焊缝的重建焊缝与真实测量数据的平均距离误差为1.10mm,圆柱对接焊缝的平均距离误差为1.33mm。重建实验结果和误差实验结果显示,本文基于立体视觉的三维重建算法可以克服焊接场景无纹理的缺点,获得精度较高的重建结果,能够满足遥控机器人任务空间的建模需要。
Welding technology would be widely used in maintenance of nuclear plants, construction and repairing operations of underwater structures and outer space aircrafts. Telerobotic welding has become the best choice to perform welding tasks in those hazardous or extreme environments instead of human operator. When performing the weld task in unstructured environment, automatic robot functions can be employed by the recontruction of robot’s task space to avoid depending too much on human operator and to improve the efficiency and security. Furthermore, according to the development need of the completely autonomous welding, the ability to perceive envirionment of weld robot should be improved. The thesis investigates 3D reconstruction of the welding robot’s task space base on stereo vision, and 3D modeling of untextured weld environment is implemented.
Stereo vision system for 3D reconstruction of telerobotic’s task space is set up. The system is calibrated and epipolar rectification is applied to the image pair. By system calibration, the mapping between the point in the robot coordinate and the pixel in the image is built. Epipolar rectification resamples the image and transforms epipolar line to image scanline, so that the complex of stereo matching algorithm is reduced.
Lack of texture in weld scene makes stereo matching harder. Active illumination is applied to add artificial texture into the scene, and both feature matching of singe image pair and image sequence matching of spacetime stereo are investigated to accomplish stereo matching.
In single image pair matching, features of corners and lines are used. Subpixel corner detection algorithm is used to detect the corner features. Man-machine interaction and autonomously corner matching are used for image pairs of peripheral equipment and simple workpiece respectively. A new line feature detection algorithm is presented to accomplish stereo matching of weld seam. The image is preprocessed by bottom hat transformation of morphology to enhance weld seam section and initial closed pixel level edge points of the weld seam are obtained by Canny detector and close operation. According to the continuity and limited width of weld seam, these edge points are filtered to eliminate wrong data. Then the filtered data is fitted to cubic smooth spline and the subpixel weld edge features are obtained. These subpixel feature points of weld seam are matched by epipolar constraint.
A multi-level dual“saw-tooth”stripe gray light patterns are designed to add artificial texture into scene and a normalized SSSD matching algorithm is presented to revise spacetime stereo to accomplish the matching of images for the untextured weld environment. The normalized SSSD algorithm improves precision by normalizing the SSD of image pairs at different times. Subpixel disparity is calculated by fitting a second-degree curve to the SSSD values. By intensity tracking of the image sequence and left right consistant check, wrong regions are removed in the disparity map and anisotropy diffusion filtering is introduced to smooth disparity map while preserving depth discontinuity.
The problem of creating 3D model of task space from disparity map or point clouds is also studied. The scene’s triangulation mesh model can be directly created from disparity map. Besides, standard geometry model of weld workpiece can be obtained by disparity map segmentation and surface fitting. The USF range image plane segmentation algorithm is revised to segment disparity map containing cylinder surface via an additional step of region merging. The segmented point clouds, which belong to different surfaces, are then fitted to different surface models. The fitting algorithms for plane, general quadric and cylinder are investigated. Eigenvector method is used for plane fitting and regulation least squares method is used for quadric fitting. For cylinder fitting, a new algorithm of collaboration of principal component analysis and nonlinear least-squares algorithm is presented. For weld seam, NURBS curve fitting is used to create model from point cloud of weld seam
For typical weld task space, the reconstruction experiments for peripheral equipment, weld workpiece and weld seam are carried out. The errors of system calibration and reconstruction are analyzed. The mean of absolute value of the calibration’s absolute error along X、Y、Z axes are [0.26 0.18 0.74]mm, respectively. Mean absolute position error for corner of peripheral equipment is 6.53mm. The fitting error for plane workpiece is 0.92mm, and the mean error of the boundary is 3.43mm. For saddle-shape workpiece, the mean fitting error is 1.35mm, and mean error for cylinders’heights and radius is 2.25mm. In reconstruction results of weld seam, the mean distance of fitted curve to the measured true datas for S-weld seam is 1.10mm, and the mean distance for cylinder butt joint weld seam is 1.33mm. The reconstruction results and errors show that the 3D reconstruction algorithm based on stereo vision can overcome the problem of lack of texture in welding scene, relative high precision can be obtained, and the algorithm can satisfy the need of creating model of task space for telerobotic welding.
引文
1 J.E. Agapakis, K. Masubuchi. Fundamental and Advances in the Development of Remote Welding Fabrication System. Welding Journal. 1986, 65(9): 21~34
2 M. Ferre, R. Aracll, M. Navas. Stereoscopic Video Images for Telerobotic Applications. Journal of Robotic Systems. 2005, 22(3):131~146
3 J.J. Conrath. Remotely Controlled Repair of Piping at Douglas Point. International Conference on Robotics and Remote Handling in the Nuclear Industry, Toronto, Canada, 1984: 112~121
4 H. Trevor, L. Trevor. ARM and Rovsim: Extending Our Reach. Industrial Robot. 1999, 26(3): 202~208
5 T.J. Larkum, D.R. Broome. Advanced Controller for an Underwater Manipulator. Proceedings of 3rd IEEE Conference on Control Application, Glasgow, Scotland, U.K., 1984: 112~121
6 B.S. Herschel, R.T. Charles. Complex Intelligent Machines. Proceedings of
18th Symposium on Energy Engineering Sciences: DOE-BES, Argonne National Lab., USA, 2000: 667~674
7张惠斌,周振丰,吴林,侯明.遥控焊接机器人系统.焊接学报. 1995, 16(3):153~157
8张惠斌.主从遥控弧焊机器人实验系统建立和操作特性研究.吉林工业大学博士学位论文. 1994
9吕伟新,张炯,樊滨温,潘少静.面向空间应用的主从与自主式遥操作系统研究.高技术通讯. 1997,(1): 27~30
10 M. Hou, S.H. Yeo, L. Wu, H.B. Zhang. On Teleoperation of an Arc Welding Robotic System. Proceedings of IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota, 1996: 1275~1280
11张连新.基于多智能体技术的机器人遥控焊接系统研究.哈尔滨工业大学博士学位论文. 2007
12李海超.焊接遥操作机器人系统及人机协作控制策略的研究.哈尔滨工业大学博士学位论文. 2006
13马颂德,张正友.计算机视觉.科学出版社, 1998
14 http://www.al911.org/wireless/triangulation_location.htm
15 http://www.qrg.northwestern.edu/projects/vss/docs/Navigation/1-what-is-triangulation.html
16 N. Ayache, F. Lustman. Trinocular Stereo Vision for Robotcs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991,13(1):73~85
17 J. Mulligan, K. Daniilidis. Real time Trinocular Stereo for Tele-Immersion.International Conference on Image Processing, 2001:959~962
18 M. Okutomi, T. Kanade. A Multiple-Baseline Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1993,15(4):353~363
19 M.Z. Brown, D. Burschka, G..D. Hager. Advances in Computational Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003, 25(8):993~1008
20 M. Pollefeys. Self-Calibration and Metric 3D Reconstruction from Uncalibrated Image Sequences. PhD Thesis. Katholieke Universiteit Leuven, Belgium. 1999
21 http://www.stockeryale.com/i/lasers/structured_light.htm
22 J. Davis, D. Nehab, R. Ramamoothi, et al. Spacetime Stereo: A Unifying Framework for Depth from Triangulation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005,27(2):296~302
23 L. Zhang, B. Curless, S. Seitz. Spacetime Stereo: Shape Recovery for Dynamic Scenes. Proceedings of Computer Vision and Pattern Recognition, 2003:367~374
24贾云德.机器视觉.科学出版社, 2000
25朱志刚.视觉导航中环境建模的研究.清华大学工学博士论文. 1997:1~2
26 M. Maurette. Mars Rover Autonomous Navigation. Autonomous Robots. 2003,14(2-3):199~208
27 Z.Y. Zhang. A Stereovision System for a Planetary Rover: Calibration, Correlation, Registration and Fusion. Machine Vision and Applications. 1997,10(1):27~34
28 M. Vergauwen, M. Pollefeys, L. V.Gool. A Stereo-Vision System for Support of Planetary Surface Exploration. Machine Vision and Applications. 2003,14(1):5~14
29 F.X. Espiau, P. Prives. Extracting Robust Features and 3D Reconstruction in Underwater Images. Proceedings of OCEANS MES/IEEE, 2001:2564~2569
30 T. Tommasini, A. Fusiello, V. Roberto. Robust Feature Tracking in Underwater Video Sequence. Proceedings of OCEANs, Nice France, 1998:46~50
31 A. Hogue, A. German, J. Zacher, M. Jenkin. Underwater 3D Mapping: Experiences and Lessons Learned. Proceeding of Canadian Conference on Computer and Robot Vision, 2006:24~31
32 C.C. Wang, S.W. Shyue, H.C. Hsu, J.S.Sue, T.C.Huang. CCD Camera Calibration for Underwater Laser Scanning System. OCEANS, MTS/IEEE Conference and Exhibition, Honolulu, Hi USA,2001:2511~2517
33 W.R. Hamel, R. L. Kress. Elements of Telerobotics Necessary for Waste Clean up Automation. Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea, 2001:393~400
34 Human Machine Cooperative Telerobotics. Final Technical Report, US Department of Energy, 2003.7
35 Robot Task Scene Analyzer. Technical Report, US Department of Energy, 2000.8
36 A. Johnson, P. Leger, R. Hoffman, M. Hebert, et al. 3-D Object Modeling and Recognition for Telerobotic Manipulation. Proceedings of IEEE Intelligent Robots and Systems, 1995:103~110
37 M. Hebert, R. Hoffman, A. Johnson, J. Osborn. Sensor-Based Interior Modeling American Nuclear Society 6th Topical Meeting on Robotics and Remote Systems February, 1995:731~737
38 A. Johnson, R. Hoffman, J. Osborn, M. Hebert. A System for Semi-Automatic Modeling of Complex Environments. International Conference on Recent Advances in 3-D Digital Imaging and Modeling, Ottawa, Ontario, 1997 :213~220
39 P. Even, L. Marce. Pyramide: an Interactive Tool for Modeling of Teleoperation Environments. International Workshop on Intelligent Robots and Systems, Tokyo Japan, 1988:725~730
40 P. Even, R. Fournier. Telerobotics Tasks Execution Based on 3D Geometic Modeling and Graphical Programming. International Conference on Systems, Man and Cybernetics. Le Touquet , France, 1993:232~137
41 P. Even, P. Gravez, E. Maillard, R. Fournier. Acquisition and Exploitation of 3D Environment Model for Computer Aided Teleoperation. Proceedings of IEEE International Workshop on Robot and Human Interaction, Pisa,Italy, 1999:261~266
42 N. Navab, et al. Cylicon: a Software Platform for the Creation and Update of Virtual Factories. IEEE International Conference on Emerging Technologies and Factory Automation, 1999:459~463
43 J. Jeffrey, Q. Charles, L. Cynthia. Advanced 3D Sensing and Visualization System for Unattended Monitoring. Sandia Report, 1998.12
44 S.W. Kwon. Human-Assisted Fitting and Matching of Objects to Sparse Point Clouds for Rapid Workspace Modeling in Construction Automation. Ph.D thesis, The University of Texas at Austin, 2003
45 S. Thayer, et.al. On-Line Stereo Vision and Graphical Interface for Decontamination and Decommissioning Applications using Advanced Servo Manipulation. Proceeding of the ANS Fifth Topical Meeting on Robotics and Remote Systems, 1993:287~294
46 A.J. Azarbayejani, et al. Recursive Estimation for CAD Model Recovery. Proceedings of the Second CAD Based Vision Workshop, 1994:90~97
47王志江,张广军,梁志敏,高洪明等.熔池表面三维检测与测量技术现状与发展趋势.焊接. 2007,(8):15~19
48 M.Y. Kim, H. Cho. Three-Dimensional Map Building for Mobile Robot Navigation Environments using a Self-Organizing Neutral Network. Journal of Robotic Systems. 2004, 21(6):323~343
49 M. Vincze, M.M. Ayromlou, et al. A System to Navigate a Robot into a ShipStructure. Machine Vision and Applications. 2003,14(1): 15~25
50 http://www.inrialpes.fr/VIGOR/Summary.html
51金建敏.弧焊机器人焊接路径预规划的研究.清华大学博士学位论文. 1996
52 S.B. Chen, X.Z. Chen, T. Qiu, J.Q. Li. Acquisition of Weld Seam Dimensional Position Information for Arc Welding Robot Based on Vision Computing. Journal of Intelligent and Robotic Systems: Theory and Applications. 2005,43(1):77~97
53王忠立.基于多极线立体匹配技术的研究.哈尔滨工业大学博士论文. 2000
54 N. Burtnyk, J. Basran. Supervisory Control of Telerobots in Unstructured Environments. Fifth International Conference on Advanced Robotics (ICAR), 1991:1420~1424
55 Z.Y. Zhang. A Flexible New Technique for Camera Calibration. MSR-TR-
98-71, Microsoft Research, 1998
56 D.W. Eggert, A. Lorossa, R.B. Fisher. Estimating 3-D Rigid Body Transformations: a Comparison of Four Major Algorithms. Machine Vision and Application. 1997,10(9):272~290
57 N. Ayache, F. Lustman. Trinocular Stereo Vision for Robotics. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991,13(1):73~85
58 O. Faugeras. Three-Dimensional Computer Vision: A Geometric Viewpoint. The MIT Press. 1991
59 A. Fusiello, E. Trucco, A. Verri. A Compact Algorithm for Rectification of Stereo Pairs. Machine Vision and Applications. 2000,12(1):16~22
60 D.V. Papadimitriou, T.J. Dennis. Epipolar Line Estimation and Rectification for Stereo Image Pairs. IEEE Transactions on Image Processing. 1996,3(4):672~676
61 L. Robert, C. Zeller, et al. Applications of Non-metric Vision to Some Vision-Guided Robotics Tasks. In Visual Navigation; From Biological System to Unmanned Ground Vehicles, Y.Aloimonos, Eds, Lawrece Erlbaum Associate, 1997:89~134
62 R. Hartley, R. Gupta. Computing Matched Epipolar Projetions. Proceedings of Computer Vision and Pattern Recognition, 1993:540~555
63 C. Loop, Z.Y. Zhang. Computing Rectfying Homographies for Stereo Vision. MSR-TR-99-21, Microsoft Research, 1999
64 R. S′ebastien, J. Meunier, J.C. Ingemar. Cylindrical Rectification to Minimize Epipolar Distortion. Proceedings of Computer Vision and Pattern Recognition, 1997:393~399
65 D. Oram. Rectification for Any Epipolar Geometry. British Machine Vision Conference, 2000:654~662
66 W.E.L Grimson, D. Marr. A Computer Implementation of a Theory ofHuman Stereo Vision. Proceedings of Image Understanding Workshop, 1979:41~47
67 S.T. Barnard, W.B. Thompson. Disparity Analysis of Image. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1980,2(4):333~340
68 Mallat. Zero-Crossing of a Wavelet Transform. IEEE Transactions on Information Theory. 1991,37(4):1019~1033
69 J.S. Lee. Multiscale Corner Detection by Using Wavelet Transform. IEEE Trans on Image Processing. 1995,4(1):100~104
70 R. Zabih, J. Woodfill. Non-Parametric Local Transforms for Computing Visual Correspondence. Proceedings of European Conference on Computer Vision, 1994:150~158
71 O. Faugeras, et al. Real Time Correlation Based Stereo: Algorithm, Implementations and Application. INRIA Research Report, No.2013, 1993
72 S. Birchfield, C. Tomasi. Depth Discontinuities by Pixel to Pixel Stereo. International Conference on Computer Vision, 1998:1073~1080
73 S. Roy, I.J. Cox. A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem. Proceedings of International Conference on Computer Vision, Bombai, 1998:492~499
74 D. Scharstein, R. Szeliski. Stereo Matching with Nonlinear Diffusion. International Journal of Computer Vision. 1998,28(2):155~174
75 J. Sun, H.Y. Shum, N.N. Zheng. Stereo Matching using Belief Propagation. Proceedings of Europain Conference on Computer Vision, 2002:510~524
76 D. Scharstein, R. Szeliski. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision. 2002,47(1):7~42. Microsoft Research Technical Report MSR-TR-2001-81, 2001
77 S.T. Barnard, M.A. Fischler. Computational Stereo. ACM Computing Surveys. 1982,42(14):553~572
78 U.R Dhond, J.K Aggarwal. Structure from Stereo—A Review. IEEE Transactions on Systems, Man and Cybernetics. 1989,19(6):1489~1510
79 A. Koschan. What is New in Computational Stereo Since 1989: A Survey of Current Stereo Papers. Technical Report 93~22, University of Berlin, 1993
80 B. Zitova, J. Flusser. Image Registration Methods: A Survey. Image and Vision Computing. 2003,21(11):977~1000
81 D.S. Tissainayagam. Assessing the Performance of Corner Detectors for Point Feature Tracking Applications. Image and Vision Computing. 2004,22(8):663~679
82 C.G. Harris. A Combined Corner and Edge Detector. Proceeding Fourth Alvey Vision Conference, Manchester, 1988:147~151
83 E. Vincent, R. Laganiere. Matching Feature Points for Telerobotics. Proceedings of International Workshop on Haptic Virtual Environments and Their Application, 2002:13~18.
84 Z.Y. Zhang, R. Deriche, O. Faugeras, Q.T Luong. A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry. Artificial Intelligence. 1995,78(1-2):87~119
85孟晓桥.摄像机自标定和三维重建中若干问题的研究.中科院自动化研究所硕士论文. 2001:36~42
86冈萨雷斯等著.数字图像处理.阮秋琦等译.第二版.北京:电子工业出版社, 2003
87 B. David. Procedures for Fitting Cubic Smoothing Splines. http://www.ssc.upenn.edu/~vr0j/ec714-99/spl_doc.pdf
88 V. Kolmogorov, R. Zabih. Computing Visual Correspondence with Occlusions using Graph Cuts. International Conference on Computer Vision, Vancouver, Canada, 2001:508~515
89 K. Morovec, R. Harvey, J. Bangham. Improving Stereo Performance in Regions of Low Texture. Proceedings of British Machine Vision Conference, Southampton, UK, 1998:822~831
90 H.B. Lan, S.L. Wood. Three Dimensional Reconstruction from Low Textured Stereo Pairs. The Thirty-Fifth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2001:376~380
91 J. Salvi, J. Pages, J. Batlle. Pattern Codification Strategies in Structured Light Systems. Pattern Recognition. 2004,37(4):827~849
92 J.L. Posdamer, M.D. Altschuler. Surface Measurement by Space-encoded Projected Beam System. Computer Graphics and Image Processing. 1982,18(1):1~17
93 B. Carrihill, R. Hummel. Experiments with the Intensity Ratio Depth Sensor. Computer Vision, Graphs and Image Processing. 1985,32:337~358
94 A. Fusiello, V. Roberto, E. Trucco. Efficient Stereo with Multiple Windowing. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1997:858~863
95 M. Shimizu, M. Okutomi. Two-Dimensional Simultaneous Subpixel Estimation for Area-Based Matching. Systems and Computers in Japan. 2005,36(2):1~11
96 M. Shimizu, M. Okutomi. Sub-pixel Estimation Error Cancellation on Area-based Matching. International Journal of Computer Vision. 2005, 63(3):207~224
97 D. Nehab, R. Rusinkiewicz, J. Davis. Improved Sub-pixel Stereo Correspondences Through Symmetric Refinement. International Conference on Computer Vision, 2005:557~563
98 J. Sun, Y. Li, S.B. Kang , et al. Symmetric Stereo Matching for Occlusion Handling. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005:399~406
99 C.L. Zitnick, T. Kanade. A Cooperative Algorithm for Stereo Matching and Occlusion Detection. IEEE Transactions on Pattern Analysis and MachineIntelligence. 2000,22(7):627~684
100 G. Egnal, R.P. Wildes. Detecting Binocular Half-Occlusions: Empirical Comparisons of Four Approaches. Proceedings of IEEE Conference on Computer Vision and Pattern Recongnition, 2000:2466~2473
101 D.E. Small. Real-Time Shape from Silhouette. Master thesis. University of Maryland. 1989:16~17
102 T.R. Jones. Feature Preserving Smoothing of 3D Surface Scans. Master Thesis, Massachusetts Institute of Technology. 2003
103 A.P. Witkin. Scale Space Filtering. Proceedings of International Joint Conference on Aritficial Intelligence, Karlsruhe, Germany, 1983:1019~1021
104 P. Perona, J. Malik. Scale-space and Edge Detection using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1990,12(7):629~639
105 M.J. Black, G. Sapiro, D.H. Marimont, et al. Robust Anisotropic Diffusion. IEEE Transactions on Image Processing. 1998, 7(3):421~432
106 J. Feddema, C. Little. Rapid World Modeling: Fitting Range Data to Geometric Primitives. Proceedings of International Conference on Robotics and Automation, Albuquerque, New Mexico, 1997:2807~2812
107 C. Little, C. Wilson. Rapid World Modeling for Robotics Technical Report 1996
108 Johnson A.E. Spin-images: a Representation for 3-D Surface Matching. PhD thesis. Carnegie Mellon University. 1997
109 S. Choi. Practical Delaunay Triangulation Algorithms for Surface Reconstruction and Related Problems. PhD thesis. The University of Texas at Austin. 2003
110 A. Hoover, G. Jean-Baptiste, X. Jiang, P. Flynn, et al. An Experimental Comparison of Range Image Segmentation Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1996,18(7):673~689
111 S. PetitJean. A Survey of Methods for Recovering Quadrics in Triangle Meshes. ACM Computing Surveys. 2002,2(34):1~61
112 R. Krishnapuram, S. Gupta. Morphological Methods for Detection and Classification of Edges in Range Images. Mathematical Imaging and Vision, 1992,2:351~375
113 N. Abdelmalek. Surface Curvatures and 3D Range Images Segmentation. Pattern Recognition. 1990,23(8):807~817
114 N. Sapidis, P. Besl. Direct Construction of Polynomial Surfaces from Dense Range Images through Region Growing. ACM Transactions on Graphics. 1995,14(2):171~200
115 K.M. Lee, P. Meer, R. H. Park. Robust Adaptive Segmentation of Range Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998,20(2):200~205
116 K. Koster, M. Spann. MIR: An Approach to Robust Clustering Application to Range Image Segmentation. IEEE Transactions on Pattern Analysis andMachine Intelligence. 2000,22(5):430~444
117 M. Oshima, Y. Shirai. Object Recognition using Three Dimensional Information. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1983,5(4):353~361
118 G. Medioni, B. Parvin. Segmentation of Range Images into Planar Surfaces by Split and Merge. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1986:415~417
119 Y. Yokoya, M. Levine. Range Image Segmentation Based on Differential Geometry: A hybrid Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989,11(6):643~649
120 A. Lejeune, F. Ferrie. Finding the Parts of Objects in Range Images. Computer Vision and Image Understanding, 1996,64(2):230~247
121 http://www.middlebury.edu/stereo
122 R.J. Schalkoff, R.M. Geist, D. Dawson. et al. Advanced Sensing and Control Techniques to Facilitate Semi-Autonomous Decommissioning of Hazardous Sites. Technical Report, Clemson University. 2000
123 C. Shakarji. Least-Squares Fitting Algorithms of the NIST Algorithm Testing System. Journal of Research of the National Institute of Standards and Technology. 1998,103(6):633~641
124 W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery. Numerical Recipes in C.傅祖云,赵海娜,丁岩石等译. 2nd Edition.电子工业出版社, 2004:318~319,359~363,363~367
125 A. Tikhonov, V. Arsenin. Solutions of Ill-Posed Problems. V.H. Winston and Sons, 1977:74~79
126 R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification.李宏东等译. 2nd Edition.机械工业出版社, 2003:94~96
127 G. Lukács, A.D. Marshall, R.R. Martin. Faithful Least-Squares Fitting of Spheres, Cylinders, Cones and Tori for Reliable Segmentation. Proceedings of Europain Conference on Computer Vision, Berlin, 1998:671~686
128 Q.G. Zhang, R.B. Greenway. Development and Implementation of a NURBS Curve Motion Interpolator. Robotics and Computer-Integrated Manufacturing. 1998,14(1):27~36
129何广忠.机器人弧焊离线编程系统及其自动编程技术的研究.哈尔滨工业大学博士学位论文. 2006
130 R.Y. Tsai. A Versatile Camera Calibration Technique for High Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses. IEEE Journal of Robotics and Automation. 1987,3(4):323~344
2 M. Ferre, R. Aracll, M. Navas. Stereoscopic Video Images for Telerobotic Applications. Journal of Robotic Systems. 2005, 22(3):131~146
3 J.J. Conrath. Remotely Controlled Repair of Piping at Douglas Point. International Conference on Robotics and Remote Handling in the Nuclear Industry, Toronto, Canada, 1984: 112~121
4 H. Trevor, L. Trevor. ARM and Rovsim: Extending Our Reach. Industrial Robot. 1999, 26(3): 202~208
5 T.J. Larkum, D.R. Broome. Advanced Controller for an Underwater Manipulator. Proceedings of 3rd IEEE Conference on Control Application, Glasgow, Scotland, U.K., 1984: 112~121
6 B.S. Herschel, R.T. Charles. Complex Intelligent Machines. Proceedings of
18th Symposium on Energy Engineering Sciences: DOE-BES, Argonne National Lab., USA, 2000: 667~674
7张惠斌,周振丰,吴林,侯明.遥控焊接机器人系统.焊接学报. 1995, 16(3):153~157
8张惠斌.主从遥控弧焊机器人实验系统建立和操作特性研究.吉林工业大学博士学位论文. 1994
9吕伟新,张炯,樊滨温,潘少静.面向空间应用的主从与自主式遥操作系统研究.高技术通讯. 1997,(1): 27~30
10 M. Hou, S.H. Yeo, L. Wu, H.B. Zhang. On Teleoperation of an Arc Welding Robotic System. Proceedings of IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota, 1996: 1275~1280
11张连新.基于多智能体技术的机器人遥控焊接系统研究.哈尔滨工业大学博士学位论文. 2007
12李海超.焊接遥操作机器人系统及人机协作控制策略的研究.哈尔滨工业大学博士学位论文. 2006
13马颂德,张正友.计算机视觉.科学出版社, 1998
14 http://www.al911.org/wireless/triangulation_location.htm
15 http://www.qrg.northwestern.edu/projects/vss/docs/Navigation/1-what-is-triangulation.html
16 N. Ayache, F. Lustman. Trinocular Stereo Vision for Robotcs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991,13(1):73~85
17 J. Mulligan, K. Daniilidis. Real time Trinocular Stereo for Tele-Immersion.International Conference on Image Processing, 2001:959~962
18 M. Okutomi, T. Kanade. A Multiple-Baseline Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1993,15(4):353~363
19 M.Z. Brown, D. Burschka, G..D. Hager. Advances in Computational Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003, 25(8):993~1008
20 M. Pollefeys. Self-Calibration and Metric 3D Reconstruction from Uncalibrated Image Sequences. PhD Thesis. Katholieke Universiteit Leuven, Belgium. 1999
21 http://www.stockeryale.com/i/lasers/structured_light.htm
22 J. Davis, D. Nehab, R. Ramamoothi, et al. Spacetime Stereo: A Unifying Framework for Depth from Triangulation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005,27(2):296~302
23 L. Zhang, B. Curless, S. Seitz. Spacetime Stereo: Shape Recovery for Dynamic Scenes. Proceedings of Computer Vision and Pattern Recognition, 2003:367~374
24贾云德.机器视觉.科学出版社, 2000
25朱志刚.视觉导航中环境建模的研究.清华大学工学博士论文. 1997:1~2
26 M. Maurette. Mars Rover Autonomous Navigation. Autonomous Robots. 2003,14(2-3):199~208
27 Z.Y. Zhang. A Stereovision System for a Planetary Rover: Calibration, Correlation, Registration and Fusion. Machine Vision and Applications. 1997,10(1):27~34
28 M. Vergauwen, M. Pollefeys, L. V.Gool. A Stereo-Vision System for Support of Planetary Surface Exploration. Machine Vision and Applications. 2003,14(1):5~14
29 F.X. Espiau, P. Prives. Extracting Robust Features and 3D Reconstruction in Underwater Images. Proceedings of OCEANS MES/IEEE, 2001:2564~2569
30 T. Tommasini, A. Fusiello, V. Roberto. Robust Feature Tracking in Underwater Video Sequence. Proceedings of OCEANs, Nice France, 1998:46~50
31 A. Hogue, A. German, J. Zacher, M. Jenkin. Underwater 3D Mapping: Experiences and Lessons Learned. Proceeding of Canadian Conference on Computer and Robot Vision, 2006:24~31
32 C.C. Wang, S.W. Shyue, H.C. Hsu, J.S.Sue, T.C.Huang. CCD Camera Calibration for Underwater Laser Scanning System. OCEANS, MTS/IEEE Conference and Exhibition, Honolulu, Hi USA,2001:2511~2517
33 W.R. Hamel, R. L. Kress. Elements of Telerobotics Necessary for Waste Clean up Automation. Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea, 2001:393~400
34 Human Machine Cooperative Telerobotics. Final Technical Report, US Department of Energy, 2003.7
35 Robot Task Scene Analyzer. Technical Report, US Department of Energy, 2000.8
36 A. Johnson, P. Leger, R. Hoffman, M. Hebert, et al. 3-D Object Modeling and Recognition for Telerobotic Manipulation. Proceedings of IEEE Intelligent Robots and Systems, 1995:103~110
37 M. Hebert, R. Hoffman, A. Johnson, J. Osborn. Sensor-Based Interior Modeling American Nuclear Society 6th Topical Meeting on Robotics and Remote Systems February, 1995:731~737
38 A. Johnson, R. Hoffman, J. Osborn, M. Hebert. A System for Semi-Automatic Modeling of Complex Environments. International Conference on Recent Advances in 3-D Digital Imaging and Modeling, Ottawa, Ontario, 1997 :213~220
39 P. Even, L. Marce. Pyramide: an Interactive Tool for Modeling of Teleoperation Environments. International Workshop on Intelligent Robots and Systems, Tokyo Japan, 1988:725~730
40 P. Even, R. Fournier. Telerobotics Tasks Execution Based on 3D Geometic Modeling and Graphical Programming. International Conference on Systems, Man and Cybernetics. Le Touquet , France, 1993:232~137
41 P. Even, P. Gravez, E. Maillard, R. Fournier. Acquisition and Exploitation of 3D Environment Model for Computer Aided Teleoperation. Proceedings of IEEE International Workshop on Robot and Human Interaction, Pisa,Italy, 1999:261~266
42 N. Navab, et al. Cylicon: a Software Platform for the Creation and Update of Virtual Factories. IEEE International Conference on Emerging Technologies and Factory Automation, 1999:459~463
43 J. Jeffrey, Q. Charles, L. Cynthia. Advanced 3D Sensing and Visualization System for Unattended Monitoring. Sandia Report, 1998.12
44 S.W. Kwon. Human-Assisted Fitting and Matching of Objects to Sparse Point Clouds for Rapid Workspace Modeling in Construction Automation. Ph.D thesis, The University of Texas at Austin, 2003
45 S. Thayer, et.al. On-Line Stereo Vision and Graphical Interface for Decontamination and Decommissioning Applications using Advanced Servo Manipulation. Proceeding of the ANS Fifth Topical Meeting on Robotics and Remote Systems, 1993:287~294
46 A.J. Azarbayejani, et al. Recursive Estimation for CAD Model Recovery. Proceedings of the Second CAD Based Vision Workshop, 1994:90~97
47王志江,张广军,梁志敏,高洪明等.熔池表面三维检测与测量技术现状与发展趋势.焊接. 2007,(8):15~19
48 M.Y. Kim, H. Cho. Three-Dimensional Map Building for Mobile Robot Navigation Environments using a Self-Organizing Neutral Network. Journal of Robotic Systems. 2004, 21(6):323~343
49 M. Vincze, M.M. Ayromlou, et al. A System to Navigate a Robot into a ShipStructure. Machine Vision and Applications. 2003,14(1): 15~25
50 http://www.inrialpes.fr/VIGOR/Summary.html
51金建敏.弧焊机器人焊接路径预规划的研究.清华大学博士学位论文. 1996
52 S.B. Chen, X.Z. Chen, T. Qiu, J.Q. Li. Acquisition of Weld Seam Dimensional Position Information for Arc Welding Robot Based on Vision Computing. Journal of Intelligent and Robotic Systems: Theory and Applications. 2005,43(1):77~97
53王忠立.基于多极线立体匹配技术的研究.哈尔滨工业大学博士论文. 2000
54 N. Burtnyk, J. Basran. Supervisory Control of Telerobots in Unstructured Environments. Fifth International Conference on Advanced Robotics (ICAR), 1991:1420~1424
55 Z.Y. Zhang. A Flexible New Technique for Camera Calibration. MSR-TR-
98-71, Microsoft Research, 1998
56 D.W. Eggert, A. Lorossa, R.B. Fisher. Estimating 3-D Rigid Body Transformations: a Comparison of Four Major Algorithms. Machine Vision and Application. 1997,10(9):272~290
57 N. Ayache, F. Lustman. Trinocular Stereo Vision for Robotics. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991,13(1):73~85
58 O. Faugeras. Three-Dimensional Computer Vision: A Geometric Viewpoint. The MIT Press. 1991
59 A. Fusiello, E. Trucco, A. Verri. A Compact Algorithm for Rectification of Stereo Pairs. Machine Vision and Applications. 2000,12(1):16~22
60 D.V. Papadimitriou, T.J. Dennis. Epipolar Line Estimation and Rectification for Stereo Image Pairs. IEEE Transactions on Image Processing. 1996,3(4):672~676
61 L. Robert, C. Zeller, et al. Applications of Non-metric Vision to Some Vision-Guided Robotics Tasks. In Visual Navigation; From Biological System to Unmanned Ground Vehicles, Y.Aloimonos, Eds, Lawrece Erlbaum Associate, 1997:89~134
62 R. Hartley, R. Gupta. Computing Matched Epipolar Projetions. Proceedings of Computer Vision and Pattern Recognition, 1993:540~555
63 C. Loop, Z.Y. Zhang. Computing Rectfying Homographies for Stereo Vision. MSR-TR-99-21, Microsoft Research, 1999
64 R. S′ebastien, J. Meunier, J.C. Ingemar. Cylindrical Rectification to Minimize Epipolar Distortion. Proceedings of Computer Vision and Pattern Recognition, 1997:393~399
65 D. Oram. Rectification for Any Epipolar Geometry. British Machine Vision Conference, 2000:654~662
66 W.E.L Grimson, D. Marr. A Computer Implementation of a Theory ofHuman Stereo Vision. Proceedings of Image Understanding Workshop, 1979:41~47
67 S.T. Barnard, W.B. Thompson. Disparity Analysis of Image. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1980,2(4):333~340
68 Mallat. Zero-Crossing of a Wavelet Transform. IEEE Transactions on Information Theory. 1991,37(4):1019~1033
69 J.S. Lee. Multiscale Corner Detection by Using Wavelet Transform. IEEE Trans on Image Processing. 1995,4(1):100~104
70 R. Zabih, J. Woodfill. Non-Parametric Local Transforms for Computing Visual Correspondence. Proceedings of European Conference on Computer Vision, 1994:150~158
71 O. Faugeras, et al. Real Time Correlation Based Stereo: Algorithm, Implementations and Application. INRIA Research Report, No.2013, 1993
72 S. Birchfield, C. Tomasi. Depth Discontinuities by Pixel to Pixel Stereo. International Conference on Computer Vision, 1998:1073~1080
73 S. Roy, I.J. Cox. A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem. Proceedings of International Conference on Computer Vision, Bombai, 1998:492~499
74 D. Scharstein, R. Szeliski. Stereo Matching with Nonlinear Diffusion. International Journal of Computer Vision. 1998,28(2):155~174
75 J. Sun, H.Y. Shum, N.N. Zheng. Stereo Matching using Belief Propagation. Proceedings of Europain Conference on Computer Vision, 2002:510~524
76 D. Scharstein, R. Szeliski. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision. 2002,47(1):7~42. Microsoft Research Technical Report MSR-TR-2001-81, 2001
77 S.T. Barnard, M.A. Fischler. Computational Stereo. ACM Computing Surveys. 1982,42(14):553~572
78 U.R Dhond, J.K Aggarwal. Structure from Stereo—A Review. IEEE Transactions on Systems, Man and Cybernetics. 1989,19(6):1489~1510
79 A. Koschan. What is New in Computational Stereo Since 1989: A Survey of Current Stereo Papers. Technical Report 93~22, University of Berlin, 1993
80 B. Zitova, J. Flusser. Image Registration Methods: A Survey. Image and Vision Computing. 2003,21(11):977~1000
81 D.S. Tissainayagam. Assessing the Performance of Corner Detectors for Point Feature Tracking Applications. Image and Vision Computing. 2004,22(8):663~679
82 C.G. Harris. A Combined Corner and Edge Detector. Proceeding Fourth Alvey Vision Conference, Manchester, 1988:147~151
83 E. Vincent, R. Laganiere. Matching Feature Points for Telerobotics. Proceedings of International Workshop on Haptic Virtual Environments and Their Application, 2002:13~18.
84 Z.Y. Zhang, R. Deriche, O. Faugeras, Q.T Luong. A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry. Artificial Intelligence. 1995,78(1-2):87~119
85孟晓桥.摄像机自标定和三维重建中若干问题的研究.中科院自动化研究所硕士论文. 2001:36~42
86冈萨雷斯等著.数字图像处理.阮秋琦等译.第二版.北京:电子工业出版社, 2003
87 B. David. Procedures for Fitting Cubic Smoothing Splines. http://www.ssc.upenn.edu/~vr0j/ec714-99/spl_doc.pdf
88 V. Kolmogorov, R. Zabih. Computing Visual Correspondence with Occlusions using Graph Cuts. International Conference on Computer Vision, Vancouver, Canada, 2001:508~515
89 K. Morovec, R. Harvey, J. Bangham. Improving Stereo Performance in Regions of Low Texture. Proceedings of British Machine Vision Conference, Southampton, UK, 1998:822~831
90 H.B. Lan, S.L. Wood. Three Dimensional Reconstruction from Low Textured Stereo Pairs. The Thirty-Fifth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2001:376~380
91 J. Salvi, J. Pages, J. Batlle. Pattern Codification Strategies in Structured Light Systems. Pattern Recognition. 2004,37(4):827~849
92 J.L. Posdamer, M.D. Altschuler. Surface Measurement by Space-encoded Projected Beam System. Computer Graphics and Image Processing. 1982,18(1):1~17
93 B. Carrihill, R. Hummel. Experiments with the Intensity Ratio Depth Sensor. Computer Vision, Graphs and Image Processing. 1985,32:337~358
94 A. Fusiello, V. Roberto, E. Trucco. Efficient Stereo with Multiple Windowing. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1997:858~863
95 M. Shimizu, M. Okutomi. Two-Dimensional Simultaneous Subpixel Estimation for Area-Based Matching. Systems and Computers in Japan. 2005,36(2):1~11
96 M. Shimizu, M. Okutomi. Sub-pixel Estimation Error Cancellation on Area-based Matching. International Journal of Computer Vision. 2005, 63(3):207~224
97 D. Nehab, R. Rusinkiewicz, J. Davis. Improved Sub-pixel Stereo Correspondences Through Symmetric Refinement. International Conference on Computer Vision, 2005:557~563
98 J. Sun, Y. Li, S.B. Kang , et al. Symmetric Stereo Matching for Occlusion Handling. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005:399~406
99 C.L. Zitnick, T. Kanade. A Cooperative Algorithm for Stereo Matching and Occlusion Detection. IEEE Transactions on Pattern Analysis and MachineIntelligence. 2000,22(7):627~684
100 G. Egnal, R.P. Wildes. Detecting Binocular Half-Occlusions: Empirical Comparisons of Four Approaches. Proceedings of IEEE Conference on Computer Vision and Pattern Recongnition, 2000:2466~2473
101 D.E. Small. Real-Time Shape from Silhouette. Master thesis. University of Maryland. 1989:16~17
102 T.R. Jones. Feature Preserving Smoothing of 3D Surface Scans. Master Thesis, Massachusetts Institute of Technology. 2003
103 A.P. Witkin. Scale Space Filtering. Proceedings of International Joint Conference on Aritficial Intelligence, Karlsruhe, Germany, 1983:1019~1021
104 P. Perona, J. Malik. Scale-space and Edge Detection using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1990,12(7):629~639
105 M.J. Black, G. Sapiro, D.H. Marimont, et al. Robust Anisotropic Diffusion. IEEE Transactions on Image Processing. 1998, 7(3):421~432
106 J. Feddema, C. Little. Rapid World Modeling: Fitting Range Data to Geometric Primitives. Proceedings of International Conference on Robotics and Automation, Albuquerque, New Mexico, 1997:2807~2812
107 C. Little, C. Wilson. Rapid World Modeling for Robotics Technical Report 1996
108 Johnson A.E. Spin-images: a Representation for 3-D Surface Matching. PhD thesis. Carnegie Mellon University. 1997
109 S. Choi. Practical Delaunay Triangulation Algorithms for Surface Reconstruction and Related Problems. PhD thesis. The University of Texas at Austin. 2003
110 A. Hoover, G. Jean-Baptiste, X. Jiang, P. Flynn, et al. An Experimental Comparison of Range Image Segmentation Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1996,18(7):673~689
111 S. PetitJean. A Survey of Methods for Recovering Quadrics in Triangle Meshes. ACM Computing Surveys. 2002,2(34):1~61
112 R. Krishnapuram, S. Gupta. Morphological Methods for Detection and Classification of Edges in Range Images. Mathematical Imaging and Vision, 1992,2:351~375
113 N. Abdelmalek. Surface Curvatures and 3D Range Images Segmentation. Pattern Recognition. 1990,23(8):807~817
114 N. Sapidis, P. Besl. Direct Construction of Polynomial Surfaces from Dense Range Images through Region Growing. ACM Transactions on Graphics. 1995,14(2):171~200
115 K.M. Lee, P. Meer, R. H. Park. Robust Adaptive Segmentation of Range Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998,20(2):200~205
116 K. Koster, M. Spann. MIR: An Approach to Robust Clustering Application to Range Image Segmentation. IEEE Transactions on Pattern Analysis andMachine Intelligence. 2000,22(5):430~444
117 M. Oshima, Y. Shirai. Object Recognition using Three Dimensional Information. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1983,5(4):353~361
118 G. Medioni, B. Parvin. Segmentation of Range Images into Planar Surfaces by Split and Merge. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1986:415~417
119 Y. Yokoya, M. Levine. Range Image Segmentation Based on Differential Geometry: A hybrid Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989,11(6):643~649
120 A. Lejeune, F. Ferrie. Finding the Parts of Objects in Range Images. Computer Vision and Image Understanding, 1996,64(2):230~247
121 http://www.middlebury.edu/stereo
122 R.J. Schalkoff, R.M. Geist, D. Dawson. et al. Advanced Sensing and Control Techniques to Facilitate Semi-Autonomous Decommissioning of Hazardous Sites. Technical Report, Clemson University. 2000
123 C. Shakarji. Least-Squares Fitting Algorithms of the NIST Algorithm Testing System. Journal of Research of the National Institute of Standards and Technology. 1998,103(6):633~641
124 W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery. Numerical Recipes in C.傅祖云,赵海娜,丁岩石等译. 2nd Edition.电子工业出版社, 2004:318~319,359~363,363~367
125 A. Tikhonov, V. Arsenin. Solutions of Ill-Posed Problems. V.H. Winston and Sons, 1977:74~79
126 R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification.李宏东等译. 2nd Edition.机械工业出版社, 2003:94~96
127 G. Lukács, A.D. Marshall, R.R. Martin. Faithful Least-Squares Fitting of Spheres, Cylinders, Cones and Tori for Reliable Segmentation. Proceedings of Europain Conference on Computer Vision, Berlin, 1998:671~686
128 Q.G. Zhang, R.B. Greenway. Development and Implementation of a NURBS Curve Motion Interpolator. Robotics and Computer-Integrated Manufacturing. 1998,14(1):27~36
129何广忠.机器人弧焊离线编程系统及其自动编程技术的研究.哈尔滨工业大学博士学位论文. 2006
130 R.Y. Tsai. A Versatile Camera Calibration Technique for High Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses. IEEE Journal of Robotics and Automation. 1987,3(4):323~344