视觉移动机器人自主导航关键技术研究
|
摘要
随着移动机器人的研究和发展,其突出的工作性能在许多领域都被广泛应用,但同时也提出了更高的要求,越来越复杂的工作环境要求移动机器人智能化程度更高,自主能力更强,尤其是自主导航能力要更好。由于模拟人眼的视觉传感器技术具有较高的智能和优势,视觉移动机器人的发展越来越受到人们的重视,并表现出良好的发展前景,特别是在军事上的应用前景更加光明。本文针对视觉移动机器人在复杂、未知环境下的自主导航问题,重点研究了视觉定位、视觉障碍物检测以及视觉道路检测等关键技术,以提高移动机器人的智能水平,同时设计并实现了视觉移动机器人软、硬件系统。
(1)视觉定位方面:研究了图像特征点的检测与匹配。针对图像特征点的检测,提出了分层快速SUSAN角点检测算法,利用角点周围像素的灰度特性,结合提升小波变换由粗到细的分层策略,快速找到角点特征,解决了SUSAN角点检测算法速度较慢的问题,为特征点的匹配打下基础。针对图像特征点的匹配,提出了一种新的RSTC不变矩特征点匹配方法,利用新构造的RSTC不变矩来度量角点的相似性,并用改进的RANSAC鲁棒估计以及外极线约束进行引导匹配,获得了较高精度的匹配结果,消除了野值匹配对所导致的长线条,较好地解决了图像特征点匹配不准确的问题。
(2)视觉障碍物检测方面,研究了视差图计算方法,提出了自适应分层粒子群稠密视差图估计算法,首先利用SIFT特征检测与匹配算法准确确定视差范围;然后根据图像和视差范围的大小分层,建立由粗及细的自适应分层图像金字塔结构,加快搜索速度、减少错误匹配;最后在优化函数中引入根据匹配窗口大小自动变化的因子来调整灰度项和平滑项的权重,并用改进的带变异算子的整数形式的PSO进行优化,克服了遗传算法搜索的盲目性以及容易陷入局部最优的缺陷,能够更快、更好的找到最优解。
(3)视觉道路检测方面,研究了图像处理中适合复杂图像处理的颜色模型,提出了SCT域归一化互信息道路检测算法,结合SCT颜色模型与人眼视觉系统的反应较接近、计算量小、抗噪能力强和归一化互信息测度鲁棒性较强、精确度较高的优点,充分利用像素点之间的关系进行加速处理,快速检测出移动机器人可行驶区域和可疑障碍物,为视觉系统障碍物的检测奠定基础。
(4)在上述研究的基础上,本文设计并实现了视觉移动机器人的软、硬件系统。根据视觉移动机器人的功能需求,在“悍马”越野模型车的基础上,安装了左右伺服减速驱动电机、摄像机安装支架以及旋转机构,设计了与之相应的硬件控制系统,并搭配摄像机、无线影音传输模块以及图像采集卡等组成双目立体视觉系统的硬件部分,从而构成了视觉移动机器人平台;在硬件平台的基础上进行了视觉系统的开发,实现了视觉移动机器人自主导航控制功能。
With the research and development of mobile robots, they are widely used, but more and more intelligence and autonomy are required to fit complex environment, so better auto navigation system is very important. As simulating human eyes, vision sensor technologies have more intelligence and advantages than other sensors, so more and more importance is added to vision mobile robots. Vision mobile robots have good development and future, especially in military. Auto navigation for vision mobile robots in complex and unknown environment has been focused on this dissertation. The key technologies of localization, obstacle detection and road detection based on vision technology for vision mobile robots have been researched to improve mobile robot’s intelligence, and a vision mobile robot system including hardware and software has also been established.
In vision localization, image feature(corners) detection and matching method have been presented. As to image feature detection, a hierarchical fast corner detection algorithm by coarse-to-fine based on SUSAN algorithm has been proposed to improve runtime. According to the gray similarity around pixels and corner property in image, firstly the theory of lifting wavelet transform and coarse-to-fine hierarchical strategy have been used to find the coarse positions of corners, then SUSAN algorithm has been used to locate the corners accurately. As to feature matching, a corner matching method based on moment invariants of RSTC(Rotation, Scale, Translation and Contrast) has been proposed to improve precision and reliability. A new moment invariant of RSTC has been constructed to describe the corner features and to measure the similarity of corner matching, and a guided matching with improved RANSAC robust estimation and epipolar line constraint has been performed. The long lines caused by wrong feature matching have been eliminated, and better matching results has been got by this method than the gray matching method.
In obstacle detection based on vision technology, a method of dense disparity map estimation using PSO algorithm with adaptive hierarchical images has been proposed to solve the stereo correspondence problem. In this method, firstly image features have been extracted and matched by SIFT algorithm, and the disparity range has been got easily and accurately. Then, according to restriction of the image size and the disparity range, the coarse to fine adaptive hierarchical image pyramids have been built to search fast and reduce wrong matching. With a regulation parameter varying with matching window used to give different power for grayness and smoothness data in optimization function while the matching window has been different in dissimilar supporting areas, an improved particle swarm optimization algorithm with variation operation for integer has been used to find the fittest solution from a set of potential disparity maps avoiding Genetic algorithm’s blind searching and easy getting in local best solutions. Experimental results on synthetic and real images have demonstrated that the proposed approach performs dense disparity estimation accurately and quickly.
In road detection based on vision technology, road detecting using normalized mutual information of SCT color model has been presented to fit for complex images after comparing and analyzing different color models. The SCT color model is not only closer to HVS(Human Vision System), but also has the characters of strong noise-rejection and good real-time performance. Normalized mutual information has the advantage of good robustness and accuracy. They have been integrated in the proposed algorithm, and image processing time has been improved by using the relationship of pixels around. So safe areas and dubious obstacles information has been provided fast for vision system by the proposed algorithm.
The hardware and software of vision mobile robot have been designed and implemented in this dissertation. Firstly, based on "Hummer" overland model car, two servo motors with planet type gearbox have been fixed on the left and right side for driving. Camera fixing bracket and rotary mechanism have been designed, and new hardware has also been designed for controlling. Moreover, with digital image acquisition card and the wireless video & audio transmitting system, the vision mobile robot platform has been built. Based on the platform, the software of vision system has been developed, and auto navigation by vision system has been implemented.
(1)视觉定位方面:研究了图像特征点的检测与匹配。针对图像特征点的检测,提出了分层快速SUSAN角点检测算法,利用角点周围像素的灰度特性,结合提升小波变换由粗到细的分层策略,快速找到角点特征,解决了SUSAN角点检测算法速度较慢的问题,为特征点的匹配打下基础。针对图像特征点的匹配,提出了一种新的RSTC不变矩特征点匹配方法,利用新构造的RSTC不变矩来度量角点的相似性,并用改进的RANSAC鲁棒估计以及外极线约束进行引导匹配,获得了较高精度的匹配结果,消除了野值匹配对所导致的长线条,较好地解决了图像特征点匹配不准确的问题。
(2)视觉障碍物检测方面,研究了视差图计算方法,提出了自适应分层粒子群稠密视差图估计算法,首先利用SIFT特征检测与匹配算法准确确定视差范围;然后根据图像和视差范围的大小分层,建立由粗及细的自适应分层图像金字塔结构,加快搜索速度、减少错误匹配;最后在优化函数中引入根据匹配窗口大小自动变化的因子来调整灰度项和平滑项的权重,并用改进的带变异算子的整数形式的PSO进行优化,克服了遗传算法搜索的盲目性以及容易陷入局部最优的缺陷,能够更快、更好的找到最优解。
(3)视觉道路检测方面,研究了图像处理中适合复杂图像处理的颜色模型,提出了SCT域归一化互信息道路检测算法,结合SCT颜色模型与人眼视觉系统的反应较接近、计算量小、抗噪能力强和归一化互信息测度鲁棒性较强、精确度较高的优点,充分利用像素点之间的关系进行加速处理,快速检测出移动机器人可行驶区域和可疑障碍物,为视觉系统障碍物的检测奠定基础。
(4)在上述研究的基础上,本文设计并实现了视觉移动机器人的软、硬件系统。根据视觉移动机器人的功能需求,在“悍马”越野模型车的基础上,安装了左右伺服减速驱动电机、摄像机安装支架以及旋转机构,设计了与之相应的硬件控制系统,并搭配摄像机、无线影音传输模块以及图像采集卡等组成双目立体视觉系统的硬件部分,从而构成了视觉移动机器人平台;在硬件平台的基础上进行了视觉系统的开发,实现了视觉移动机器人自主导航控制功能。
With the research and development of mobile robots, they are widely used, but more and more intelligence and autonomy are required to fit complex environment, so better auto navigation system is very important. As simulating human eyes, vision sensor technologies have more intelligence and advantages than other sensors, so more and more importance is added to vision mobile robots. Vision mobile robots have good development and future, especially in military. Auto navigation for vision mobile robots in complex and unknown environment has been focused on this dissertation. The key technologies of localization, obstacle detection and road detection based on vision technology for vision mobile robots have been researched to improve mobile robot’s intelligence, and a vision mobile robot system including hardware and software has also been established.
In vision localization, image feature(corners) detection and matching method have been presented. As to image feature detection, a hierarchical fast corner detection algorithm by coarse-to-fine based on SUSAN algorithm has been proposed to improve runtime. According to the gray similarity around pixels and corner property in image, firstly the theory of lifting wavelet transform and coarse-to-fine hierarchical strategy have been used to find the coarse positions of corners, then SUSAN algorithm has been used to locate the corners accurately. As to feature matching, a corner matching method based on moment invariants of RSTC(Rotation, Scale, Translation and Contrast) has been proposed to improve precision and reliability. A new moment invariant of RSTC has been constructed to describe the corner features and to measure the similarity of corner matching, and a guided matching with improved RANSAC robust estimation and epipolar line constraint has been performed. The long lines caused by wrong feature matching have been eliminated, and better matching results has been got by this method than the gray matching method.
In obstacle detection based on vision technology, a method of dense disparity map estimation using PSO algorithm with adaptive hierarchical images has been proposed to solve the stereo correspondence problem. In this method, firstly image features have been extracted and matched by SIFT algorithm, and the disparity range has been got easily and accurately. Then, according to restriction of the image size and the disparity range, the coarse to fine adaptive hierarchical image pyramids have been built to search fast and reduce wrong matching. With a regulation parameter varying with matching window used to give different power for grayness and smoothness data in optimization function while the matching window has been different in dissimilar supporting areas, an improved particle swarm optimization algorithm with variation operation for integer has been used to find the fittest solution from a set of potential disparity maps avoiding Genetic algorithm’s blind searching and easy getting in local best solutions. Experimental results on synthetic and real images have demonstrated that the proposed approach performs dense disparity estimation accurately and quickly.
In road detection based on vision technology, road detecting using normalized mutual information of SCT color model has been presented to fit for complex images after comparing and analyzing different color models. The SCT color model is not only closer to HVS(Human Vision System), but also has the characters of strong noise-rejection and good real-time performance. Normalized mutual information has the advantage of good robustness and accuracy. They have been integrated in the proposed algorithm, and image processing time has been improved by using the relationship of pixels around. So safe areas and dubious obstacles information has been provided fast for vision system by the proposed algorithm.
The hardware and software of vision mobile robot have been designed and implemented in this dissertation. Firstly, based on "Hummer" overland model car, two servo motors with planet type gearbox have been fixed on the left and right side for driving. Camera fixing bracket and rotary mechanism have been designed, and new hardware has also been designed for controlling. Moreover, with digital image acquisition card and the wireless video & audio transmitting system, the vision mobile robot platform has been built. Based on the platform, the software of vision system has been developed, and auto navigation by vision system has been implemented.
引文
[1]徐国保,尹怡欣,周美娟.智能移动机器人技术现状及展望[J].机器人技术与应用, 2007, (2): 29-34.
[2]于今,饶冀,闫军涛.基于双目立体视觉的壁面清洗机器人障碍物检测研究[J].传感器与微系统, 2007, 26(7): 67-69.
[3] Gage DW. UGV History 101: A Brief History of Unmanned Ground Vehicle (UGV) development Efforts[J]. Unmanned Systems Magazine, 1995, 13(3): 1–9.
[4] Nilsson NJ. A mobile automaton: An application of artificial intelligence techniques[A]. Proceedings of the 1st International Joint Conference on Artificial Intelligence[C]. 1969: 509–520.
[5] Moravec HP. Towards automatic visual obstacle avoidance[A]. Proceedings of the 5th International Joint Conference on Artificial Intelligence[C]. Cambridge, MA, USA: 1977: 584-590.
[6] Moravec HP. Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover[D]. California: Stanford University, 1980.
[7] Maki JN, Bell JF, Herkenhoff KE, et al. Mars Exploration Rover Engineering Cameras[J]. Journal of Geophysical Research, 2003, 108(E12): 1-24.
[8] Grudic G, Mulligan J, Otte M, et al. Online Learning of Multiple Perceptual Models for Navigation in Unknown Terrain[A]. Proceedings of the International Conference on Field and Service Robotics[C]. Chamonix, France: 2007, 42: 411-420.
[9] Klafter RD. Mobile robots, research and development[A]. Dorf R C, Nof S Y. International Encyclopedia of Robotics:Appfications and Automation[C]. Wiley, New York: John Wiley & Sons Inc, 1988: 920-943.
[10] Schafer H, Luksch T, Berns K. Obstacle Detection and Avoidance for Mobile Outdoor Robotics[A]. EOS Conference on Industrial Imaging and Machine Vision[C]. 2005: 1-4.
[11] Schafer H, Proetzsch M, Braun T, et al. RAVON - A Robust Autonomous Vehicle for Off-road Navigation[A]. European Land Robot Trial - ELROB[C]. 2006: 1-15.
[12]李瑞峰,高彤,闫国荣,等.双臂作业型智能服务机器人的研制[J].华中科技大学学报(自然科学版), 2004, 32(S1): 179-181.
[13] Fung WK, Leung YY, Chow MK, et al. Development of a hospital service robot fortransporting task[A]. Proceedings of IEEE International Conference on Robotics, Intelligent Systems and Signal Processing(RISSP)[C]. 2003, 1: 628-633.
[14] Xiong Y, Shafer SA. Depth from focusing and defocusing[A]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. New York, NY, USA: 1993: 68-73.
[15] Souhila K, Karim A. Optical Flow based robot obstacle avoidance[J]. International Journal of Advanced Robotic Systems, 2007, 4(1): 13-16.
[16] Ghidary SS, Nakata Y, Takamori T, et al. Human detection and localization at indoor environment by homerobot[A]. IEEE International Conference on Systems, Man, and Cybernetics[C]. Nashville, TN, USA: 2000, 2: 1360-1365.
[17] Nourbakhsh I, Andre D, Tomasi C, et al. Mobile robot obstacle avoidance via depth from focus[J]. Robotics and Autonomous Systems, 1997, 22(2): 151-158.
[18] Bing L, Bingrong H. Active Tracking with Pan-Tilt-Zoom Camera[J]. High Technology Letters, 2003, 9(1): 68-71.
[19]王剑,王元庆.基于双焦成像的单眼立体视觉算法[J].机器人, 2007, 29(1): 41-44.
[20] Moravec HP. Visual mapping by a robot rover[A]. Proceedings of the 6th International Joint Conference on Artificial Intelligence[C]. Tokyo, Japan: 1979: 599-601.
[21] Yachida M, Kitamura Y, Kimachi M. Trinocular vision: New approach for correspondence problem[A]. Proceedings of 8th Intentional Conference on Pattern Recognition[C]. Paris,France: 1986: 1041-1044.
[22] Ayache N, Lustman F. Fast and reliable passive trinocular stereovision[A]. Proceedings of the International Conference in Computer Vision[C]. 1987, 87: 422-427.
[23]张少敏,沈俊.用金字塔图法匹配边界图象的三眼立体视觉方法[J].机器人, 1994, 16(3): 172-175.
[24] Stein F, Medioni G. Map-based localization using the panoramic horizon[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. Nice, France: 1992: 2631-2637.
[25]刘亚,艾海舟,徐光佑.基于主运动分析的野外视觉侦察系统--运动目标检测,跟踪及全景图的生成[J].机器人, 2001, 23(3): 250-256.
[26]张尧,陈卫东.一个基于全景视觉的移动机器人导航系统的设计与实现[J].机器人, 2005, 27(2): 173-177.
[27] Doi M, Suzuki K, Hashimoto S. Integrated Communicative Robot“BUGNOID”[A].Proceedings of 11th IEEE International Workshop on Robot and Human Interactive Communication[C]. Berlin, Germany: 2002: 259-264.
[28] Whittaker W, Bapna D, Maimone M, et al. Atacama Desert Trek: A Planetary Analog Field Experiment[A]. Proceedings of the Fourth International Symposium on Artificial Intelligence, Robotics and Automation for Space (i-SAIRAS'97)[C]. Tokyo, Japan: 1997: 14-16.
[29]朱志刚,徐光佑,林学訚,等.多尺度全覆盖视觉导航方法[J].机器人, 1998, 20(4): 266-272.
[30]游素亚,徐光祐.立体视觉研究的现状与进展[J].中国图象图形学报, 1997, 2(1): 17-24.
[31] Marr D.视觉计算理论[M].姚国正,刘磊,汪云九,译.北京:科学出版社, 1988.
[32]龙甫荟,郑南宁.计算机视觉模型的研究与发展[J].信息与控制, 1997, 26(2): 112-116.
[33] Marr D, Poggio T. Cooperative computation of stereo disparity[J]. Science, 1976, 194(4262): 283-287.
[34] Jones GA. Constraint, Optimization, and Hierarchy: Reviewing Stereoscopic Correspondence of Complex Features[J]. Computer Vision and Image Understanding, 1997, 65(1): 57-78.
[35] Forstmann S, Kanou Y, Ohya J, et al. Real-Time Stereo by using Dynamic Programming[A]. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop[C]. 2004(3): 29-36.
[36] Ranade S, Rosenfeld A. Point pattern matching by relaxation[J]. Pattern Recognition, 1980, 12(4): 269-275.
[37] Kolmogorov V, Zabih R. Computing visual correspondence with occlusions using graph cuts[A]. Proceedings ot the International Conference on Computer Vision[C]. Vancouver, BC, Canada: 2001, 2: 508-515.
[38] Henkel RD, Jordan MI, Kearns MJ, et al. A Simple and Fast Neural Network Approach to Stereovision[A]. Proceedings of the Conference on Advances in neural information processing systems[C]. Denver, Colorado, USA: 1998: 808-814.
[39] Agrawal A, Ansari N, Hou ES. Evolutionary programming for fast and robust point pattern matching[A]. IEEE International Conference on Neural Networks[C]. Orlando, FL, USA: 1994, 3: 1777-1782.
[40] Cohen L, Vinet L, Sander PT, et al. Hierarchical region based stereo matching[A]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. San Diego, CA, USA: 1989: 416-421.
[41] Venkateswar V, Chellappa R. Hierarchical stereo and motion correspondence using featuregroupings[J]. International Journal of Computer Vision, 1995, 15(3): 245-269.
[42] Stefano LD, Mattoccia S. Fast stereo matching for the VIDET system using a general purpose processor with multimedia extensions[A]. Proceedings of the Fifth IEEE International Workshop on Computer Architectures for Machine Perception[C]. 2000: 356-362.
[43] Faugeras O, Vieville T, Theron E, et al. Real-time correlation-based stereo: algorithm, implementations and applications[R]. France: INRIA, 1993.
[44] Hariyama M, Takeuchi T, Kameyama M. VLSI processor for reliable stereo matching based on adaptive window-size selection[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. 2001, 2: 1168-1173.
[45] Dunn P, Corke P. Real-time stereopsis using FPGAs[A]. Proceedings of the 7th International Workshop on Field-Programmable Logic and Applications[C]. 1997, 1304: 400-409.
[46] Konolige K. Small Vision Systems: Hardware and Implementation[A]. Eighth International Symposium on Robotics Research[C]. Japan: 1997: 111-116.
[47] Broida TJ, Chellappa R. Kinematics and structure of a rigid object from a sequence of noisy images[A]. Proc IEEE workshop on motion representation and analysis[C]. Charleston, USA: 1986: 95-100.
[48] Hung YS, Ho HT. A Kalman filter approach to direct depth estimation incorporatingsurface structure[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(6): 570-575.
[49]钟志光,易建强,赵冬斌,等.基于运动视的移动机器人定位方法[J].机器人, 2004, 26(4): 325-329.
[50] Hager GD, Chang WC, Morse AS. Robot hand-eye coordination based on stereo vision[J]. IEEE Control Systems Magazine, 1995, 15(1): 30-39.
[51] Mallet A, Lacroix S, Gallo L. Position estimation in outdoor environments using pixel tracking and stereovision[A]. IEEE International Conference on Robotics and Automation[C]. San Francisco, USA: 2000, 4: 3519-3524.
[52]刘伟军,董再励,郝颖明,等.基于立体视觉的移动机器人自主导航定位系统[J].高技术通讯, 2001, 11(10): 91-94.
[53]李瑞峰,李庆喜.机器人双目视觉系统的标定与定位算法[J].哈尔滨工业大学学报, 2007, 39(11): 1719-1722.
[54]张祖勋.数字影像定位与核线排列[J].武汉测绘学院学报, 1983, 1(1): 14-27.
[55] Longuet-higgins HC. A computer algorithm for reconstructing a scene from two projections[J].Nature, 1981, 293(10): 133-135.
[56] Hough PV. Methods and means for recognition complex pattern[P]. US Patent: 3,069,654, 1962.
[57] Duda RO, Hart PE. Use of the Hough transformation to detect lines and curves in pictures[J]. Communications of the ACM, 1972, 15(1): 11-15.
[58] Ballard DH. Generalizing the Hough transform to detect arbitrary shapes[J]. Pattern Recognition, 1981, 13(2): 111-122.
[59] Canny J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.
[60] Mikolajczyk K, Schmid C. An affine invariant interest point detector[J]. Proc. ECCV, 2002, 1: 128-142.
[61] Mikolajczyk K, Schmid C. Scale & Affine Invariant Interest Point Detectors[J]. International Journal of Computer Vision, 2004, 60(1): 63-86.
[62] Matas J, Chum O, Urban M, et al. Robust wide-baseline stereo from maximally stable extremal regions[J]. Image and Vision Computing, 2004, 22(10): 761-767.
[63] Kadir T, Zisserman A, Brady M. An Affine Invariant Salient Region Detector[A]. Proceedings of the 8th European Conference on Computer Vision[C]. Prague, Czech Republic: 2004: 345-457.
[64] Wurtz RP, Lourens T. Corner detection in color images through a multiscale combination of end-stopped cortical cells[J]. Image and Vision Computing, 2000, 18(6-7): 531-541.
[65] Freeman H, Davis LS. A Corner-Finding Algorithm for Chain-Coded Curves[J]. Transactions on Computers, 1977, 100(26): 297-303.
[66] Liu HC, Srinath MD. Corner detection from chain-code[J]. Pattern Recognition, 1990, 23(1-2): 51-68.
[67] Wang MJ, Wu WY, Huang LK, et al. Corner detection using bending value[J]. Pattern Recognition Letters, 1995, 16(6): 575-583.
[68] Pikaz A, Dinstein I. Optimal polygonal approximation of digital curves[J]. Pattern Recognition, 1995, 28(3): 373-379.
[69] Sarfraz M, Asim MR, Masood A. Piecewise Polygonal Approximation of Digital Curves[A]. Proceedings of the International Conference on Information Visualisation[C]. IEEE Computer Society, 2004: 991-996.
[70] Masood A, Haq SA. A novel approach to polygonal approximation of digital curves[J]. Journalof Visual Communication and Image Representation, 2007, 18(3): 264-274.
[71] Singh A, Shneier M. Grey level corner detection: a generalization and a robust real time implementation[J]. Computer Vision, Graphics, and Image Processing, 1990, 51(1): 54-69.
[72] Shen F, Wang H. Real Time Gray Level Corner Detector[A]. Proceedings of 6th International Conference on Control, Automation, Robotics and Vision[C]. Singapore: 2000: 1-6.
[73] Baker S, Nayar SK, Murase H. Parametric Feature Detection[J]. International Journal of Computer Vision, 1998, 27(1): 27-50.
[74] Deriche R, Blaszka T, Antipolis IS. Recovering and characterizing image features using an efficient model based approach[A]. Proceeding of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. New York, NY, USA: 1993: 530-535.
[75] Mayhew JEW, Frisby JP. Psychophysical and computational studies towards a theory of human stereopsis[J]. Artificial Intelligence, 1981, (17): 349-385.
[76] Kovesi P. Phase congruency detects corners and edges[A]. Proceedings of DICTA 2003[C]. Sydney: 2003: 309-318.
[77] Sebe N, Lew MS. Comparing salient point detectors[J]. Pattern Recognition Letters, 2003, 24(1-3): 89-96.
[78] Quddus A, Gabbouj M. Wavelet-based corner detection technique using optimal scale[J]. Pattern Recognition Letters, 2002, 23(1): 215-220.
[79] Mokhtarian F, Suomela R. Curvature Scale Space for Robust Image Corner Detection[A]. Proceedings of the 14th International Conference on Pattern Recognition[C]. Brisbane, Australia: IEEE Computer Society, 1998, 2: 1819-1821.
[80] Mokhtarian F, Suomela R. Robust Image Corner Detection Through Curvature Scale Space[J]. IEEE transactions on pattern analysis and machine intelligence, 1998, 20(12): 1376-1381.
[81] Lowe DG. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[82] Ke Y, Sukthankar R. PCA-SIFT: A more distinctive representation for local image descriptors[A]. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. 2004, 2: 506-513.
[83] Beaudet PR. Rotationally invariant image operators[A]. Proceeding of the International Conference on Pattern Recognition[C]. Tokyo, Japan: 1978: 579-583.
[84] Harris C, Stephens M. A combined corner and edge detector[A]. Proceedings of the fourthAlvey vision conference[C]. Manchester, UK: Alvey vision conference, 1988: 147-151.
[85] Smith SM, Brady JM. SUSAN-A New Approach to Low Level Image Processing[J]. International Journal of Computer Vision, 1997, 23(1): 45-78.
[86] Luhmann T, Altrogge G. Interest Operators for Image Matching[A]. International Archives of Photogrammetry and Remote Sensing[C]. 1986, 26(3/2): 459-474.
[87] Trajkovic M, Hedley M. Fast corner detection[J]. Image and Vision Computing, 1998, 16(2): 75-87.
[88] Wang H, Brady M. Real-time corner detection algorithm for motion estimation[J]. Image and Vision Computing, 1995, 13(9): 695-703.
[89] Zheng Z, Wang H, Khwang TE. Analysis of gray level corner detection[J]. Pattern Recognition Letters, 1999, 20(2): 149-162.
[90]杨杨,张田文.角点检测算法评价方法的研究[J].哈尔滨工业大学学报, 1998, 30(2): 7-10.
[91] Schmid C, Mohr R, Bauckhage C. Evaluation of Interest Point Detectors[J]. International Journal of Computer Vision, 2000, 37(2): 151-172.
[92]张春森.基于点特征匹配的SUSAN, Harris算子比较[J].西安科技大学学报, 2007, 27(4): 608-611.
[93] Daubechies I, Sweldens W. Factoring wavelet transforms into lifting steps[J]. Journal of Fourier Analysis and Applications, 1998, 4(3): 247-269.
[94]陈武凡.小波分析及其在图像处理中的应用[M].北京:科学出版社, 2002.
[95] Baumberg A. Reliable feature matching across widely separated views[A]. Proceedings of the Interational Conference on Computer Vision and Pattern Recognition[C]. Hilton Head Island, SC, USA: 2000: 774-781.
[96] Schmid C, Mohr R. Local Grayvalue Invariants for Image Retrieval[J]. IEEE transactions on pattern analysis and machine intelligence, 1997, 19(5): 530-535.
[97]徐斌,卢朝阳.基于灰度差分不变量的点特征匹配[J].计算机工程与科学, 2002, 24(5): 74-77.
[98] Montesinos P, Gouet V, Deriche R, et al. Matching color uncalibrated images using differential invariants[J]. Image and Vision Computing, 2000, 18(9): 659-671.
[99] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts[J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(4): 509-522.
[100] Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630.
[101] Van GL, Moons T, Ungureanu D. Affine/Photometric Invariants for Planar Intensity Patterns[A]. Proceedings of the 4th European Conference on Computer Vision[C]. Cambridge, UK: 1996, 1: 642-651.
[102] Hu MK. Visual pattern recognition by moment invariants[J]. IEEE Transactions on Information Theory, 1962, 8(2): 179-187.
[103] Wong RY, Hall EL. Scene matching with invariant moments[J]. Computer Graphics and Image Processing, 1978, 8(1): 16-24.
[104]丁明跃,常金玲.不变矩算法研究[J].数据采集与处理, 1992, 7(1): 1-9.
[105] Maitra S. Moment invariants[J]. Proceedings of the IEEE, 1979, 67(4): 697-699.
[106]李惠光,姚磊,李国友,等.基于改进的阶次规正不变矩小目标识别新方法[J].计算机仿真, 2006, 23(1): 168-171.
[107]李鹏,朱宏辉.一种改进的不变矩方法在图像目标识别中的应用[J].交通与计算机, 2004, 22(2): 88-90.
[108]杜亚娟,潘泉,张洪才.一种新的不变矩特征在图像识别中的应用[J].系统工程与电子技术, 1999, 21(10): 71-74.
[109]王连亮,陈怀新.图像识别的RSTC不变矩[J].数据采集与处理, 2006, 21(2): 225-229.
[110] Huber PJ. Robust Statistics[M]. Wiley, New York, 1981.
[111] Fischler MA, Bolles RC. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.
[112] Rousseeuw PJ, Leroy AM. Robust Regression and Outlier Detection[M]. Wiley, New York, 1987.
[113] Stewart CV. MINPRAN: A New Robust Estimator for Computer Vision[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,, 1995, 17(10): 925-938.
[114] Torr PHS, Zisserman A. MLESAC: a new robust estimator with application to estimating image geometry[J]. Computer Vision and Image Understanding, 2000, 78(1): 138-156.
[115] Torr PHS. Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting[J]. International Journal of Computer Vision, 2002, 50(1): 35-61.
[116] Hartley RI. In defense of the eight-point algorithm[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997, 19(6): 580-593.
[117] Hartley RI. Projective reconstruction and invariants from multiple images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(10): 1036-1041.
[118] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision[M]. Cambridge University Press, 2003.
[119] Sull S, Sridhar B, Center NA, et al. Model-based obstacle detection from image sequences[A]. Proceedings of the 1995 International Conference on Image Processing[C]. Washington, DC, USA: 1995, 2(2): 647-650.
[120] Denasi R, Quaglia G. Early obstacle detection using region segmentation and model-based edge grouping[A]. Proceedings of IEEE International Conference on Intelligent Vehicles[C]. Stuttgart, Germany: 1998: 257-262.
[121]李斌,王荣本,郭克友.基于机器视觉的智能车辆障碍物检测方法研究[J].公路交通科技, 2002, 19(4): 126-129.
[122] Turk M, Wolfe WJ, Marquina N. Color road segmentation and video obstacle detection[A]. SPIE Conference on Mobile Robots[C]. Cambridge, MA, USA: 1986: 136-142.
[123] Matsumoto Y, Inaba M, Inoue H. Visual navigation using view-sequenced route representation[A]. Proceedings of IEEE International Conference on Robotics and Automation[C]. Minneapolis, MN, USA: 1996, 1: 83-88.
[124]崔星,闫清东.基于帧间差分方法的道路车辆检测系统[J].微计算机信息, 2007, 23(10): 117-119.
[125] Darrell T, Wohn K. Pyramid based depth from focus[A]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. Ann Arbor, MI, USA: 1988: 504-509.
[126] Nourbakhsh LR, Andre D, Tomasi C, et al. Mobile robot obstacle avoidance via depth from focus[J]. Robotics and Autonomous Systems, 1997, 22: 151-158.
[127]尤富强,陈兴林,强文义.一种机器人避障控制策略[J].自动化技术与应用, 2002, 21(5): 31-33.
[128]朱云芳,王贻术,杜歆.静态环境中基于光流的障碍物检测[J].浙江大学学报(工学版), 2008, 42(6): 923-926.
[129]杨文杰,胡明昊,杨静宇.一种基于光流的障碍物估计算法[J].计算机工程与应用, 2006, 18(5): 80-81.
[130] Young GS, Hong TH, Herman M, et al. Obstacle detection for a vehicle using optical flow[A]. Proceedings of the Intelligent Vehicles Symposium[C]. Detroit, MI, USA: 1992: 185-190.
[131] Low T, Wyeth G. Obstacle detection using optical flow[A]. Australasian Conference on Robotics and Automation[C]. Sydney, Australia: 2005: 1-10.
[132] Broggi A, Caraffi C, Fedriga RI, et al. Obstacle Detection with Stereo Vision for Off-RoadVehicle Navigation[A]. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. San Diego, USA: IEEE Computer Society, 2005, 3: 65-72.
[133] Olson CF, Matthies LH, Schoppers M, et al. Rover navigation using stereo ego-motion[J]. Robotics and Autonomous Systems, 2003, 43(4): 215-229.
[134]魏怡,李宇,胡舒姝.面向复杂背景中彩色立体图像的障碍物检测[J].自动化技术与应用, 2007, 26(7): 23-27.
[135] Julesz B. Binocular depth perception of computer-generated patterns[J]. Bell System Technical Journal, 1960, 39: 1125-1162.
[136]章毓晋.图像工程(下册):图像理解与机器视觉[M].北京:清华大学出版社, 1999.
[137] Sonka M, Hlavac V, Boyle R.图像处理,分析与机器视觉(第二版)[M].艾海舟,武勃,译.北京:人民邮电出版社, 2003.
[138] Barnard ST, Fischler MA. Computational Stereo[J]. ACM Computing Surveys, 1982, 14(4): 553-572.
[139] Dhond UR, Aggarwal JK. Structure from stereo-a review[J]. IEEE Transactions on Systems, Man and Cybernetics, 1989, 19(6): 1489-1510.
[140] Scharstein D, Szeliski R. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms[J]. International Journal of Computer Vision, 2002, 47(1): 7-42.
[141] Poggio T, Torre V, Koch C. Computational vision and regularization theory[J]. Nature, 1985, 317(6035): 314-319.
[142]江军,於文雪,舒华忠.鲍威尔和模拟退火优化算法结合的多分辨率三维图像配准[J].生物医学工程研究, 2004, 23(3): 176-178.
[143] Ruichek Y, Postaire JG. Real-time neural vision for obstacle detection using linear cameras[A]. Proceedings of the Intelligent Vehicles' 95 Symposium[C]. Detroit, MI, USA: 1995: 524-529.
[144] Stevenson RL, Schmitz BE, Delp EJ. Discontinuity preserving regularization of inverse visual problems[J]. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24(3): 455-469.
[145]胡海峰,熊银根.一种基于Hopfield网络的立体匹配方法[J].中国图象图形学报, 2004, 9(6): 729-736.
[146]徐彦君,杜利民,侯自强,等.一种用于立体匹配的改进的神经网络方法[J].中国图象图形学报, 1998, 3(10): 845-848.
[147]周文晖,林丽莉,顾伟康.基于协作Hopfield网络的迭代立体匹配算法[J].传感技术学报, 2007, 20(4): 917-920.
[148] Chai J, Ma S. An evolutionary framework for stereo correspondence[A]. Proceedings of Fourteenth International Conference on Pattern Recognition[C]. Brisbane, Qld., Australia: 1998, 1(1): 841-844.
[149] Kim YS, Han KP, Lee EJ, et al. Robust 3-D depth estimation using genetic algorithm in stereo image pairs[A]. IEEE Asia Pacific Conference on Circuits and Systems[C]. Seoul, South Korea: 1996: 357-360.
[150] Saito H, Mori M. Application of genetic algorithms to stereo matching of images[J]. Pattern Recognition Letters, 1995, 16(8): 815-821.
[151] Wang B, Chung R, Shen CL. Genetic algorithm-based stereo vision with no block-partitioning of input images[A]. Proceedings of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation[C]. Kobe, Japan: 2003, 2: 830-836.
[152]张礼兵,金菊良,刘丽.基于实数编码的免疫遗传算法研究[J].运筹与管理, 2004, 13(4): 17-20.
[153] Alexandrov VV, Gorsky ND, Mysko SN. A fast technique for recursive scene matching using pyramids[J]. Pattern recognition letters, 1985, 3(6): 413-419.
[154] Lin CW, Fei EY, Chen YC. Hierarchical disparity estimation using spatial correlation[J]. Consumer Electronics, IEEE Transactions on, 1998, 44(3): 630-637.
[155] Cohen L, Vinet L, Sander PT, et al. Hierarchical region based stereo matching[A]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. San Diego, CA, USA: 1989: 416-421.
[156] Barnard ST. Stochastic stereo matching over scale[J]. International Journal of Computer Vision, 1989, 3(1): 17-32.
[157] Van MG, Vergauwen M, Pollefeys M, et al. A hierarchical stereo algorithm using dynamic programming[A]. Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision[C]. Kauai, HI, USA: 2001: 166-174.
[158] Yoon S, Min D, Sohn K. Fast Dense Stereo Matching Using Adaptive Window in Hierarchical Framework[J]. LECTURE NOTES IN COMPUTER SCIENCE, 2006, 4292: 316-325.
[159] Kanade T, Okutomi M. A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(9): 920-932.
[160]周秀芝,文贡坚,王润生.自适应窗口快速立体匹配[J].计算机学报, 2006, 29(3): 473-479.
[161]刘波,王凌,金以慧,等.微粒群优化算法研究进展[J].化工自动化及仪表, 2005, 32(3): 1-7.
[162]杨维,李歧强.粒子群优化算法综述[J].中国工程科学, 2004, 6(5): 87-94.
[163]张荣沂.一种新的集群优化方法--粒子群优化算法[J].黑龙江工程学院学报(自然科学版), 2004, 18(4): 34-37.
[164] Shi Y. Particle swarm optimization[J]. IEEE Neural Networks Society, 2004, 11(2): 8-13.
[165] Clerc M. The swarm and the queen: towards a deterministic and adaptiveparticle swarm optimization[A]. Proceedings of the 1999 Congress on Evolutionary Computation[C]. Washington, DC, USA: 1999, 3: 1951-1957.
[166] Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particleswarm optimization[A]. Proceedings of the 2000 Congress on Evolutionary Computation[C]. La Jolla, CA, USA: 2000, 1: 84-88.
[167]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004, 40(6): 4-6.
[168]王彪.计算机视觉技术在低空突防与精确打击中的应用研究[D].南京航空航天大学, 2004.
[169] Aufrere R, Chapuis R, Chausse F. A model-driven approach for real-time road recognition[J]. Machine Vision and Applications, 2001, 13(2): 95-107.
[170] Redmill KA. A simple vision system for lane keeping[A]. IEEE Conference on Intelligent Transportation System[C]. Boston, MA, USA: 1997: 212-217.
[171]孙爽滋,谷欣超,杨勇,等.一种基于结构化道路的单车道线检测算法[J].长春理工大学学报(自然科学版), 2008, 28(2): 112-114.
[172]赵颖,孙群,陈兵旗,等.基于机器视觉的非结构化道路导航路径检测方法[J].农业机械学报, 2007, 38(6): 201-204.
[173]赵俊梅,张利平.非结构化道路的检测和跟踪[J].工业控制计算机, 2008, 21(4): 54-55.
[174] Crisman JD, Thorpe CE. UNSCARF-a color vision system for the detection of unstructuredroads[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. Sacramento, CA, USA: 1991, 3: 2496-2501.
[175]吴东晖.智能移动机器人的视觉导航技术[D].浙江大学, 2001.
[176]杨明,陆建业,王宏,等.基于视觉的道路跟踪[J].模式识别与人工智能, 2001, 14(2): 186-193.
[177] Hong TH, Chang T, Rasmussen C, et al. Road detection and tracking for autonomous mobile robots[A]. Proc. of SPIE Aeroscience Conference[C]. Orlando, Florida: 2002, 4715: 1-5.
[178] Foedisch M, Takeuchi A. Adaptive road detection through continuous environment learning[A]. Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop[C]. IEEE Computer Society Washington, DC, USA, 2004: 16-21.
[179]安成万,张永谦,李桂芝,等.基于分量直方图的移动机器人视觉图像自适应分割[J].机器人, 2004, 26(6): 524-528.
[180]杨文杰,胡明昊,杨静宇.一种快速的基于边缘的道路检测算法[J].计算机科学, 2006, 33(5): 257-260.
[181]李大杰,柴毅,尹宏鹏,等.一种基于彩色图像边缘检测的道路检测方法[J].计算机工程与应用, 2008, 44(28): 177-179.
[182] Turk M, Morgenthaler DG, Gremban KD, et al. VITS-A vision system for autonomous land vehicle navigation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(3): 342-361.
[183] Crisman JD, Thorpe CE. SCARF: a color vision system that tracks roads and intersections[J]. IEEE Transactions on Robotics and Automation, 1993, 9(1): 49-58.
[184] Tremeau A, Borel N. A region growing and merging algorithm to color segmentation[J]. Pattern Recognition, 1997, 30(7): 1191-1203.
[185] Shih FY, Cheng S. Automatic seeded region growing for color image segmentation[J]. Image and Vision Computing, 2005, 23(10): 877-886.
[186]孔俊,王佳男,谷文祥,等.基于区域的自动种子区域生长法的彩色图像分割算法[J].东北师大学报(自然科学版), 2008, 40(4): 47-51.
[187] Cheng HD, Jiang XH, Sun Y, et al. Color image segmentation: advances and prospects[J]. Pattern Recognition, 2001, 34(12): 2259-2281.
[188]陶霖密,徐光佑.机器视觉中的颜色问题及应用[J].科学通报, 2001, 46(3): 178-190.
[189] Schetselaar EM. Fusion by the IHS transform: should we use cylindrical or spherical coordinates[J]. International Journal of Remote Sensing, 1998, 19(4): 759-765.
[190] Van WJ, Gevers T. Robust optical flow from photometric invariants[A]. Proceedings of International Conference on Image Processing[C]. 2004, 3: 1835-1838.
[191] Robin RM.人工智能机器人学导论[M].杜军平,吴立成,胡金春,译.北京:电子工业出版社, 2004.
[192] Umbaugh SE, Moss RH, Stoecker WV. Automatic color segmentation of images with application to detection of variegated coloring in skin tumors[J]. IEEE Engineering in Medicine and Biology Magazine, 1989, 8(4): 43-50.
[193]马建光,施坤林.基于图像相似性测量的移动机器人定位研究[J].探测与控制学报, 2002, 24(4): 51-54.
[194] Studholme C, Hawkes DJ, Hill DL. Normalized entropy measure for multimodality image alignment[A]. Proceedings of SPIE, Medical Imaging 1998: Image Processing[C]. San Diego, CA, USA: 1998, 3338: 132-143.
[195] Studholme C, Hill DL, Hawkes DJ. An overlap invariant entropy measure of 3D medical image alignment[J]. Pattern Recognition, 1999, 32(1): 71-86.
[196] Maes F, Collignon A, Vandermeulen D, et al. Multimodality image registration by maximization of mutual information[J]. Medical Imaging, IEEE Transactions on, 1997, 16(2): 187-198.
[197]曹蹊渺.基于互信息的图像配准算法研究[D].北京交通大学, 2008.
[198]闻芳,周杰,张长水,等.基于局部线性映射神经网络的亮度补偿的彩色人脸检测[J].清华大学学报(自然科学报), 1999, 39(7): 37-40.
[199]梁晓辉,游志胜.自适应的彩色图像光照补偿新方法[J].光电工程, 2006, 33(2): 94-97.
[200] Wettergreen D, Bapna D, Maimone M, et al. Developing Nomad for robotic exploration of the Atacama Desert[J]. ROB AUTOM SYST, 1999, 26(2): 127-148.
[201] Seal JR, Bailey DG, Gupta GS. Depth Perception with a Single Camera[A]. Proceedings of International Conference on Sensing Technology[C]. Palmerston North, New Zealand: 2005: 96-101.
[202]朱志刚,林学訚,石定机,等.基于重投影变换的实时障碍物检测视觉系统[J].计算机研究与发展, 1999, 36(1): 77-84.
[203]熊超,陆起涌,田小芳.基于DirectShow的多线程视频采集系统[J].计算机工程, 2006, (20): 221-223.
[204]陈玥,蔡自兴.基于DirectShow技术的移动机器人视频采集系统[J].华中科技大学学报(自然科学版), 2004, (S1): 87-89.
[205] Zhengyou Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
[206]陈爱华,高诚辉,何炳蔚.计算机视觉中的摄像机标定方法[J].中国工程机械学报, 2006, (4): 498-504.
[207]郑南宁.计算机视觉与模式识别[M].北京:国防工业出版社, 1998.
[208]李庆忠,陈显华,顾伟康.基于彩色立体视觉的障碍物快速检测方法[J].计算机科学, 2003, 30(9): 72-75.
[209]梅园,孙怀江,夏德深.一种基于改进后模板的图像快速细化算法[J].中国图象图形学报, 2006, 11(9): 1306-1311.
[210]白强.基于视觉反馈的智能小车系统研究[D].中南大学, 2007.
[211]赵毅红.可翻转移动机器人运动控制及动力学分析[D].南京航空航天大学, 2008.
[2]于今,饶冀,闫军涛.基于双目立体视觉的壁面清洗机器人障碍物检测研究[J].传感器与微系统, 2007, 26(7): 67-69.
[3] Gage DW. UGV History 101: A Brief History of Unmanned Ground Vehicle (UGV) development Efforts[J]. Unmanned Systems Magazine, 1995, 13(3): 1–9.
[4] Nilsson NJ. A mobile automaton: An application of artificial intelligence techniques[A]. Proceedings of the 1st International Joint Conference on Artificial Intelligence[C]. 1969: 509–520.
[5] Moravec HP. Towards automatic visual obstacle avoidance[A]. Proceedings of the 5th International Joint Conference on Artificial Intelligence[C]. Cambridge, MA, USA: 1977: 584-590.
[6] Moravec HP. Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover[D]. California: Stanford University, 1980.
[7] Maki JN, Bell JF, Herkenhoff KE, et al. Mars Exploration Rover Engineering Cameras[J]. Journal of Geophysical Research, 2003, 108(E12): 1-24.
[8] Grudic G, Mulligan J, Otte M, et al. Online Learning of Multiple Perceptual Models for Navigation in Unknown Terrain[A]. Proceedings of the International Conference on Field and Service Robotics[C]. Chamonix, France: 2007, 42: 411-420.
[9] Klafter RD. Mobile robots, research and development[A]. Dorf R C, Nof S Y. International Encyclopedia of Robotics:Appfications and Automation[C]. Wiley, New York: John Wiley & Sons Inc, 1988: 920-943.
[10] Schafer H, Luksch T, Berns K. Obstacle Detection and Avoidance for Mobile Outdoor Robotics[A]. EOS Conference on Industrial Imaging and Machine Vision[C]. 2005: 1-4.
[11] Schafer H, Proetzsch M, Braun T, et al. RAVON - A Robust Autonomous Vehicle for Off-road Navigation[A]. European Land Robot Trial - ELROB[C]. 2006: 1-15.
[12]李瑞峰,高彤,闫国荣,等.双臂作业型智能服务机器人的研制[J].华中科技大学学报(自然科学版), 2004, 32(S1): 179-181.
[13] Fung WK, Leung YY, Chow MK, et al. Development of a hospital service robot fortransporting task[A]. Proceedings of IEEE International Conference on Robotics, Intelligent Systems and Signal Processing(RISSP)[C]. 2003, 1: 628-633.
[14] Xiong Y, Shafer SA. Depth from focusing and defocusing[A]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. New York, NY, USA: 1993: 68-73.
[15] Souhila K, Karim A. Optical Flow based robot obstacle avoidance[J]. International Journal of Advanced Robotic Systems, 2007, 4(1): 13-16.
[16] Ghidary SS, Nakata Y, Takamori T, et al. Human detection and localization at indoor environment by homerobot[A]. IEEE International Conference on Systems, Man, and Cybernetics[C]. Nashville, TN, USA: 2000, 2: 1360-1365.
[17] Nourbakhsh I, Andre D, Tomasi C, et al. Mobile robot obstacle avoidance via depth from focus[J]. Robotics and Autonomous Systems, 1997, 22(2): 151-158.
[18] Bing L, Bingrong H. Active Tracking with Pan-Tilt-Zoom Camera[J]. High Technology Letters, 2003, 9(1): 68-71.
[19]王剑,王元庆.基于双焦成像的单眼立体视觉算法[J].机器人, 2007, 29(1): 41-44.
[20] Moravec HP. Visual mapping by a robot rover[A]. Proceedings of the 6th International Joint Conference on Artificial Intelligence[C]. Tokyo, Japan: 1979: 599-601.
[21] Yachida M, Kitamura Y, Kimachi M. Trinocular vision: New approach for correspondence problem[A]. Proceedings of 8th Intentional Conference on Pattern Recognition[C]. Paris,France: 1986: 1041-1044.
[22] Ayache N, Lustman F. Fast and reliable passive trinocular stereovision[A]. Proceedings of the International Conference in Computer Vision[C]. 1987, 87: 422-427.
[23]张少敏,沈俊.用金字塔图法匹配边界图象的三眼立体视觉方法[J].机器人, 1994, 16(3): 172-175.
[24] Stein F, Medioni G. Map-based localization using the panoramic horizon[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. Nice, France: 1992: 2631-2637.
[25]刘亚,艾海舟,徐光佑.基于主运动分析的野外视觉侦察系统--运动目标检测,跟踪及全景图的生成[J].机器人, 2001, 23(3): 250-256.
[26]张尧,陈卫东.一个基于全景视觉的移动机器人导航系统的设计与实现[J].机器人, 2005, 27(2): 173-177.
[27] Doi M, Suzuki K, Hashimoto S. Integrated Communicative Robot“BUGNOID”[A].Proceedings of 11th IEEE International Workshop on Robot and Human Interactive Communication[C]. Berlin, Germany: 2002: 259-264.
[28] Whittaker W, Bapna D, Maimone M, et al. Atacama Desert Trek: A Planetary Analog Field Experiment[A]. Proceedings of the Fourth International Symposium on Artificial Intelligence, Robotics and Automation for Space (i-SAIRAS'97)[C]. Tokyo, Japan: 1997: 14-16.
[29]朱志刚,徐光佑,林学訚,等.多尺度全覆盖视觉导航方法[J].机器人, 1998, 20(4): 266-272.
[30]游素亚,徐光祐.立体视觉研究的现状与进展[J].中国图象图形学报, 1997, 2(1): 17-24.
[31] Marr D.视觉计算理论[M].姚国正,刘磊,汪云九,译.北京:科学出版社, 1988.
[32]龙甫荟,郑南宁.计算机视觉模型的研究与发展[J].信息与控制, 1997, 26(2): 112-116.
[33] Marr D, Poggio T. Cooperative computation of stereo disparity[J]. Science, 1976, 194(4262): 283-287.
[34] Jones GA. Constraint, Optimization, and Hierarchy: Reviewing Stereoscopic Correspondence of Complex Features[J]. Computer Vision and Image Understanding, 1997, 65(1): 57-78.
[35] Forstmann S, Kanou Y, Ohya J, et al. Real-Time Stereo by using Dynamic Programming[A]. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop[C]. 2004(3): 29-36.
[36] Ranade S, Rosenfeld A. Point pattern matching by relaxation[J]. Pattern Recognition, 1980, 12(4): 269-275.
[37] Kolmogorov V, Zabih R. Computing visual correspondence with occlusions using graph cuts[A]. Proceedings ot the International Conference on Computer Vision[C]. Vancouver, BC, Canada: 2001, 2: 508-515.
[38] Henkel RD, Jordan MI, Kearns MJ, et al. A Simple and Fast Neural Network Approach to Stereovision[A]. Proceedings of the Conference on Advances in neural information processing systems[C]. Denver, Colorado, USA: 1998: 808-814.
[39] Agrawal A, Ansari N, Hou ES. Evolutionary programming for fast and robust point pattern matching[A]. IEEE International Conference on Neural Networks[C]. Orlando, FL, USA: 1994, 3: 1777-1782.
[40] Cohen L, Vinet L, Sander PT, et al. Hierarchical region based stereo matching[A]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. San Diego, CA, USA: 1989: 416-421.
[41] Venkateswar V, Chellappa R. Hierarchical stereo and motion correspondence using featuregroupings[J]. International Journal of Computer Vision, 1995, 15(3): 245-269.
[42] Stefano LD, Mattoccia S. Fast stereo matching for the VIDET system using a general purpose processor with multimedia extensions[A]. Proceedings of the Fifth IEEE International Workshop on Computer Architectures for Machine Perception[C]. 2000: 356-362.
[43] Faugeras O, Vieville T, Theron E, et al. Real-time correlation-based stereo: algorithm, implementations and applications[R]. France: INRIA, 1993.
[44] Hariyama M, Takeuchi T, Kameyama M. VLSI processor for reliable stereo matching based on adaptive window-size selection[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. 2001, 2: 1168-1173.
[45] Dunn P, Corke P. Real-time stereopsis using FPGAs[A]. Proceedings of the 7th International Workshop on Field-Programmable Logic and Applications[C]. 1997, 1304: 400-409.
[46] Konolige K. Small Vision Systems: Hardware and Implementation[A]. Eighth International Symposium on Robotics Research[C]. Japan: 1997: 111-116.
[47] Broida TJ, Chellappa R. Kinematics and structure of a rigid object from a sequence of noisy images[A]. Proc IEEE workshop on motion representation and analysis[C]. Charleston, USA: 1986: 95-100.
[48] Hung YS, Ho HT. A Kalman filter approach to direct depth estimation incorporatingsurface structure[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(6): 570-575.
[49]钟志光,易建强,赵冬斌,等.基于运动视的移动机器人定位方法[J].机器人, 2004, 26(4): 325-329.
[50] Hager GD, Chang WC, Morse AS. Robot hand-eye coordination based on stereo vision[J]. IEEE Control Systems Magazine, 1995, 15(1): 30-39.
[51] Mallet A, Lacroix S, Gallo L. Position estimation in outdoor environments using pixel tracking and stereovision[A]. IEEE International Conference on Robotics and Automation[C]. San Francisco, USA: 2000, 4: 3519-3524.
[52]刘伟军,董再励,郝颖明,等.基于立体视觉的移动机器人自主导航定位系统[J].高技术通讯, 2001, 11(10): 91-94.
[53]李瑞峰,李庆喜.机器人双目视觉系统的标定与定位算法[J].哈尔滨工业大学学报, 2007, 39(11): 1719-1722.
[54]张祖勋.数字影像定位与核线排列[J].武汉测绘学院学报, 1983, 1(1): 14-27.
[55] Longuet-higgins HC. A computer algorithm for reconstructing a scene from two projections[J].Nature, 1981, 293(10): 133-135.
[56] Hough PV. Methods and means for recognition complex pattern[P]. US Patent: 3,069,654, 1962.
[57] Duda RO, Hart PE. Use of the Hough transformation to detect lines and curves in pictures[J]. Communications of the ACM, 1972, 15(1): 11-15.
[58] Ballard DH. Generalizing the Hough transform to detect arbitrary shapes[J]. Pattern Recognition, 1981, 13(2): 111-122.
[59] Canny J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.
[60] Mikolajczyk K, Schmid C. An affine invariant interest point detector[J]. Proc. ECCV, 2002, 1: 128-142.
[61] Mikolajczyk K, Schmid C. Scale & Affine Invariant Interest Point Detectors[J]. International Journal of Computer Vision, 2004, 60(1): 63-86.
[62] Matas J, Chum O, Urban M, et al. Robust wide-baseline stereo from maximally stable extremal regions[J]. Image and Vision Computing, 2004, 22(10): 761-767.
[63] Kadir T, Zisserman A, Brady M. An Affine Invariant Salient Region Detector[A]. Proceedings of the 8th European Conference on Computer Vision[C]. Prague, Czech Republic: 2004: 345-457.
[64] Wurtz RP, Lourens T. Corner detection in color images through a multiscale combination of end-stopped cortical cells[J]. Image and Vision Computing, 2000, 18(6-7): 531-541.
[65] Freeman H, Davis LS. A Corner-Finding Algorithm for Chain-Coded Curves[J]. Transactions on Computers, 1977, 100(26): 297-303.
[66] Liu HC, Srinath MD. Corner detection from chain-code[J]. Pattern Recognition, 1990, 23(1-2): 51-68.
[67] Wang MJ, Wu WY, Huang LK, et al. Corner detection using bending value[J]. Pattern Recognition Letters, 1995, 16(6): 575-583.
[68] Pikaz A, Dinstein I. Optimal polygonal approximation of digital curves[J]. Pattern Recognition, 1995, 28(3): 373-379.
[69] Sarfraz M, Asim MR, Masood A. Piecewise Polygonal Approximation of Digital Curves[A]. Proceedings of the International Conference on Information Visualisation[C]. IEEE Computer Society, 2004: 991-996.
[70] Masood A, Haq SA. A novel approach to polygonal approximation of digital curves[J]. Journalof Visual Communication and Image Representation, 2007, 18(3): 264-274.
[71] Singh A, Shneier M. Grey level corner detection: a generalization and a robust real time implementation[J]. Computer Vision, Graphics, and Image Processing, 1990, 51(1): 54-69.
[72] Shen F, Wang H. Real Time Gray Level Corner Detector[A]. Proceedings of 6th International Conference on Control, Automation, Robotics and Vision[C]. Singapore: 2000: 1-6.
[73] Baker S, Nayar SK, Murase H. Parametric Feature Detection[J]. International Journal of Computer Vision, 1998, 27(1): 27-50.
[74] Deriche R, Blaszka T, Antipolis IS. Recovering and characterizing image features using an efficient model based approach[A]. Proceeding of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. New York, NY, USA: 1993: 530-535.
[75] Mayhew JEW, Frisby JP. Psychophysical and computational studies towards a theory of human stereopsis[J]. Artificial Intelligence, 1981, (17): 349-385.
[76] Kovesi P. Phase congruency detects corners and edges[A]. Proceedings of DICTA 2003[C]. Sydney: 2003: 309-318.
[77] Sebe N, Lew MS. Comparing salient point detectors[J]. Pattern Recognition Letters, 2003, 24(1-3): 89-96.
[78] Quddus A, Gabbouj M. Wavelet-based corner detection technique using optimal scale[J]. Pattern Recognition Letters, 2002, 23(1): 215-220.
[79] Mokhtarian F, Suomela R. Curvature Scale Space for Robust Image Corner Detection[A]. Proceedings of the 14th International Conference on Pattern Recognition[C]. Brisbane, Australia: IEEE Computer Society, 1998, 2: 1819-1821.
[80] Mokhtarian F, Suomela R. Robust Image Corner Detection Through Curvature Scale Space[J]. IEEE transactions on pattern analysis and machine intelligence, 1998, 20(12): 1376-1381.
[81] Lowe DG. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[82] Ke Y, Sukthankar R. PCA-SIFT: A more distinctive representation for local image descriptors[A]. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. 2004, 2: 506-513.
[83] Beaudet PR. Rotationally invariant image operators[A]. Proceeding of the International Conference on Pattern Recognition[C]. Tokyo, Japan: 1978: 579-583.
[84] Harris C, Stephens M. A combined corner and edge detector[A]. Proceedings of the fourthAlvey vision conference[C]. Manchester, UK: Alvey vision conference, 1988: 147-151.
[85] Smith SM, Brady JM. SUSAN-A New Approach to Low Level Image Processing[J]. International Journal of Computer Vision, 1997, 23(1): 45-78.
[86] Luhmann T, Altrogge G. Interest Operators for Image Matching[A]. International Archives of Photogrammetry and Remote Sensing[C]. 1986, 26(3/2): 459-474.
[87] Trajkovic M, Hedley M. Fast corner detection[J]. Image and Vision Computing, 1998, 16(2): 75-87.
[88] Wang H, Brady M. Real-time corner detection algorithm for motion estimation[J]. Image and Vision Computing, 1995, 13(9): 695-703.
[89] Zheng Z, Wang H, Khwang TE. Analysis of gray level corner detection[J]. Pattern Recognition Letters, 1999, 20(2): 149-162.
[90]杨杨,张田文.角点检测算法评价方法的研究[J].哈尔滨工业大学学报, 1998, 30(2): 7-10.
[91] Schmid C, Mohr R, Bauckhage C. Evaluation of Interest Point Detectors[J]. International Journal of Computer Vision, 2000, 37(2): 151-172.
[92]张春森.基于点特征匹配的SUSAN, Harris算子比较[J].西安科技大学学报, 2007, 27(4): 608-611.
[93] Daubechies I, Sweldens W. Factoring wavelet transforms into lifting steps[J]. Journal of Fourier Analysis and Applications, 1998, 4(3): 247-269.
[94]陈武凡.小波分析及其在图像处理中的应用[M].北京:科学出版社, 2002.
[95] Baumberg A. Reliable feature matching across widely separated views[A]. Proceedings of the Interational Conference on Computer Vision and Pattern Recognition[C]. Hilton Head Island, SC, USA: 2000: 774-781.
[96] Schmid C, Mohr R. Local Grayvalue Invariants for Image Retrieval[J]. IEEE transactions on pattern analysis and machine intelligence, 1997, 19(5): 530-535.
[97]徐斌,卢朝阳.基于灰度差分不变量的点特征匹配[J].计算机工程与科学, 2002, 24(5): 74-77.
[98] Montesinos P, Gouet V, Deriche R, et al. Matching color uncalibrated images using differential invariants[J]. Image and Vision Computing, 2000, 18(9): 659-671.
[99] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts[J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(4): 509-522.
[100] Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630.
[101] Van GL, Moons T, Ungureanu D. Affine/Photometric Invariants for Planar Intensity Patterns[A]. Proceedings of the 4th European Conference on Computer Vision[C]. Cambridge, UK: 1996, 1: 642-651.
[102] Hu MK. Visual pattern recognition by moment invariants[J]. IEEE Transactions on Information Theory, 1962, 8(2): 179-187.
[103] Wong RY, Hall EL. Scene matching with invariant moments[J]. Computer Graphics and Image Processing, 1978, 8(1): 16-24.
[104]丁明跃,常金玲.不变矩算法研究[J].数据采集与处理, 1992, 7(1): 1-9.
[105] Maitra S. Moment invariants[J]. Proceedings of the IEEE, 1979, 67(4): 697-699.
[106]李惠光,姚磊,李国友,等.基于改进的阶次规正不变矩小目标识别新方法[J].计算机仿真, 2006, 23(1): 168-171.
[107]李鹏,朱宏辉.一种改进的不变矩方法在图像目标识别中的应用[J].交通与计算机, 2004, 22(2): 88-90.
[108]杜亚娟,潘泉,张洪才.一种新的不变矩特征在图像识别中的应用[J].系统工程与电子技术, 1999, 21(10): 71-74.
[109]王连亮,陈怀新.图像识别的RSTC不变矩[J].数据采集与处理, 2006, 21(2): 225-229.
[110] Huber PJ. Robust Statistics[M]. Wiley, New York, 1981.
[111] Fischler MA, Bolles RC. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.
[112] Rousseeuw PJ, Leroy AM. Robust Regression and Outlier Detection[M]. Wiley, New York, 1987.
[113] Stewart CV. MINPRAN: A New Robust Estimator for Computer Vision[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,, 1995, 17(10): 925-938.
[114] Torr PHS, Zisserman A. MLESAC: a new robust estimator with application to estimating image geometry[J]. Computer Vision and Image Understanding, 2000, 78(1): 138-156.
[115] Torr PHS. Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting[J]. International Journal of Computer Vision, 2002, 50(1): 35-61.
[116] Hartley RI. In defense of the eight-point algorithm[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997, 19(6): 580-593.
[117] Hartley RI. Projective reconstruction and invariants from multiple images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(10): 1036-1041.
[118] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision[M]. Cambridge University Press, 2003.
[119] Sull S, Sridhar B, Center NA, et al. Model-based obstacle detection from image sequences[A]. Proceedings of the 1995 International Conference on Image Processing[C]. Washington, DC, USA: 1995, 2(2): 647-650.
[120] Denasi R, Quaglia G. Early obstacle detection using region segmentation and model-based edge grouping[A]. Proceedings of IEEE International Conference on Intelligent Vehicles[C]. Stuttgart, Germany: 1998: 257-262.
[121]李斌,王荣本,郭克友.基于机器视觉的智能车辆障碍物检测方法研究[J].公路交通科技, 2002, 19(4): 126-129.
[122] Turk M, Wolfe WJ, Marquina N. Color road segmentation and video obstacle detection[A]. SPIE Conference on Mobile Robots[C]. Cambridge, MA, USA: 1986: 136-142.
[123] Matsumoto Y, Inaba M, Inoue H. Visual navigation using view-sequenced route representation[A]. Proceedings of IEEE International Conference on Robotics and Automation[C]. Minneapolis, MN, USA: 1996, 1: 83-88.
[124]崔星,闫清东.基于帧间差分方法的道路车辆检测系统[J].微计算机信息, 2007, 23(10): 117-119.
[125] Darrell T, Wohn K. Pyramid based depth from focus[A]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. Ann Arbor, MI, USA: 1988: 504-509.
[126] Nourbakhsh LR, Andre D, Tomasi C, et al. Mobile robot obstacle avoidance via depth from focus[J]. Robotics and Autonomous Systems, 1997, 22: 151-158.
[127]尤富强,陈兴林,强文义.一种机器人避障控制策略[J].自动化技术与应用, 2002, 21(5): 31-33.
[128]朱云芳,王贻术,杜歆.静态环境中基于光流的障碍物检测[J].浙江大学学报(工学版), 2008, 42(6): 923-926.
[129]杨文杰,胡明昊,杨静宇.一种基于光流的障碍物估计算法[J].计算机工程与应用, 2006, 18(5): 80-81.
[130] Young GS, Hong TH, Herman M, et al. Obstacle detection for a vehicle using optical flow[A]. Proceedings of the Intelligent Vehicles Symposium[C]. Detroit, MI, USA: 1992: 185-190.
[131] Low T, Wyeth G. Obstacle detection using optical flow[A]. Australasian Conference on Robotics and Automation[C]. Sydney, Australia: 2005: 1-10.
[132] Broggi A, Caraffi C, Fedriga RI, et al. Obstacle Detection with Stereo Vision for Off-RoadVehicle Navigation[A]. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. San Diego, USA: IEEE Computer Society, 2005, 3: 65-72.
[133] Olson CF, Matthies LH, Schoppers M, et al. Rover navigation using stereo ego-motion[J]. Robotics and Autonomous Systems, 2003, 43(4): 215-229.
[134]魏怡,李宇,胡舒姝.面向复杂背景中彩色立体图像的障碍物检测[J].自动化技术与应用, 2007, 26(7): 23-27.
[135] Julesz B. Binocular depth perception of computer-generated patterns[J]. Bell System Technical Journal, 1960, 39: 1125-1162.
[136]章毓晋.图像工程(下册):图像理解与机器视觉[M].北京:清华大学出版社, 1999.
[137] Sonka M, Hlavac V, Boyle R.图像处理,分析与机器视觉(第二版)[M].艾海舟,武勃,译.北京:人民邮电出版社, 2003.
[138] Barnard ST, Fischler MA. Computational Stereo[J]. ACM Computing Surveys, 1982, 14(4): 553-572.
[139] Dhond UR, Aggarwal JK. Structure from stereo-a review[J]. IEEE Transactions on Systems, Man and Cybernetics, 1989, 19(6): 1489-1510.
[140] Scharstein D, Szeliski R. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms[J]. International Journal of Computer Vision, 2002, 47(1): 7-42.
[141] Poggio T, Torre V, Koch C. Computational vision and regularization theory[J]. Nature, 1985, 317(6035): 314-319.
[142]江军,於文雪,舒华忠.鲍威尔和模拟退火优化算法结合的多分辨率三维图像配准[J].生物医学工程研究, 2004, 23(3): 176-178.
[143] Ruichek Y, Postaire JG. Real-time neural vision for obstacle detection using linear cameras[A]. Proceedings of the Intelligent Vehicles' 95 Symposium[C]. Detroit, MI, USA: 1995: 524-529.
[144] Stevenson RL, Schmitz BE, Delp EJ. Discontinuity preserving regularization of inverse visual problems[J]. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24(3): 455-469.
[145]胡海峰,熊银根.一种基于Hopfield网络的立体匹配方法[J].中国图象图形学报, 2004, 9(6): 729-736.
[146]徐彦君,杜利民,侯自强,等.一种用于立体匹配的改进的神经网络方法[J].中国图象图形学报, 1998, 3(10): 845-848.
[147]周文晖,林丽莉,顾伟康.基于协作Hopfield网络的迭代立体匹配算法[J].传感技术学报, 2007, 20(4): 917-920.
[148] Chai J, Ma S. An evolutionary framework for stereo correspondence[A]. Proceedings of Fourteenth International Conference on Pattern Recognition[C]. Brisbane, Qld., Australia: 1998, 1(1): 841-844.
[149] Kim YS, Han KP, Lee EJ, et al. Robust 3-D depth estimation using genetic algorithm in stereo image pairs[A]. IEEE Asia Pacific Conference on Circuits and Systems[C]. Seoul, South Korea: 1996: 357-360.
[150] Saito H, Mori M. Application of genetic algorithms to stereo matching of images[J]. Pattern Recognition Letters, 1995, 16(8): 815-821.
[151] Wang B, Chung R, Shen CL. Genetic algorithm-based stereo vision with no block-partitioning of input images[A]. Proceedings of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation[C]. Kobe, Japan: 2003, 2: 830-836.
[152]张礼兵,金菊良,刘丽.基于实数编码的免疫遗传算法研究[J].运筹与管理, 2004, 13(4): 17-20.
[153] Alexandrov VV, Gorsky ND, Mysko SN. A fast technique for recursive scene matching using pyramids[J]. Pattern recognition letters, 1985, 3(6): 413-419.
[154] Lin CW, Fei EY, Chen YC. Hierarchical disparity estimation using spatial correlation[J]. Consumer Electronics, IEEE Transactions on, 1998, 44(3): 630-637.
[155] Cohen L, Vinet L, Sander PT, et al. Hierarchical region based stereo matching[A]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. San Diego, CA, USA: 1989: 416-421.
[156] Barnard ST. Stochastic stereo matching over scale[J]. International Journal of Computer Vision, 1989, 3(1): 17-32.
[157] Van MG, Vergauwen M, Pollefeys M, et al. A hierarchical stereo algorithm using dynamic programming[A]. Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision[C]. Kauai, HI, USA: 2001: 166-174.
[158] Yoon S, Min D, Sohn K. Fast Dense Stereo Matching Using Adaptive Window in Hierarchical Framework[J]. LECTURE NOTES IN COMPUTER SCIENCE, 2006, 4292: 316-325.
[159] Kanade T, Okutomi M. A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(9): 920-932.
[160]周秀芝,文贡坚,王润生.自适应窗口快速立体匹配[J].计算机学报, 2006, 29(3): 473-479.
[161]刘波,王凌,金以慧,等.微粒群优化算法研究进展[J].化工自动化及仪表, 2005, 32(3): 1-7.
[162]杨维,李歧强.粒子群优化算法综述[J].中国工程科学, 2004, 6(5): 87-94.
[163]张荣沂.一种新的集群优化方法--粒子群优化算法[J].黑龙江工程学院学报(自然科学版), 2004, 18(4): 34-37.
[164] Shi Y. Particle swarm optimization[J]. IEEE Neural Networks Society, 2004, 11(2): 8-13.
[165] Clerc M. The swarm and the queen: towards a deterministic and adaptiveparticle swarm optimization[A]. Proceedings of the 1999 Congress on Evolutionary Computation[C]. Washington, DC, USA: 1999, 3: 1951-1957.
[166] Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particleswarm optimization[A]. Proceedings of the 2000 Congress on Evolutionary Computation[C]. La Jolla, CA, USA: 2000, 1: 84-88.
[167]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004, 40(6): 4-6.
[168]王彪.计算机视觉技术在低空突防与精确打击中的应用研究[D].南京航空航天大学, 2004.
[169] Aufrere R, Chapuis R, Chausse F. A model-driven approach for real-time road recognition[J]. Machine Vision and Applications, 2001, 13(2): 95-107.
[170] Redmill KA. A simple vision system for lane keeping[A]. IEEE Conference on Intelligent Transportation System[C]. Boston, MA, USA: 1997: 212-217.
[171]孙爽滋,谷欣超,杨勇,等.一种基于结构化道路的单车道线检测算法[J].长春理工大学学报(自然科学版), 2008, 28(2): 112-114.
[172]赵颖,孙群,陈兵旗,等.基于机器视觉的非结构化道路导航路径检测方法[J].农业机械学报, 2007, 38(6): 201-204.
[173]赵俊梅,张利平.非结构化道路的检测和跟踪[J].工业控制计算机, 2008, 21(4): 54-55.
[174] Crisman JD, Thorpe CE. UNSCARF-a color vision system for the detection of unstructuredroads[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. Sacramento, CA, USA: 1991, 3: 2496-2501.
[175]吴东晖.智能移动机器人的视觉导航技术[D].浙江大学, 2001.
[176]杨明,陆建业,王宏,等.基于视觉的道路跟踪[J].模式识别与人工智能, 2001, 14(2): 186-193.
[177] Hong TH, Chang T, Rasmussen C, et al. Road detection and tracking for autonomous mobile robots[A]. Proc. of SPIE Aeroscience Conference[C]. Orlando, Florida: 2002, 4715: 1-5.
[178] Foedisch M, Takeuchi A. Adaptive road detection through continuous environment learning[A]. Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop[C]. IEEE Computer Society Washington, DC, USA, 2004: 16-21.
[179]安成万,张永谦,李桂芝,等.基于分量直方图的移动机器人视觉图像自适应分割[J].机器人, 2004, 26(6): 524-528.
[180]杨文杰,胡明昊,杨静宇.一种快速的基于边缘的道路检测算法[J].计算机科学, 2006, 33(5): 257-260.
[181]李大杰,柴毅,尹宏鹏,等.一种基于彩色图像边缘检测的道路检测方法[J].计算机工程与应用, 2008, 44(28): 177-179.
[182] Turk M, Morgenthaler DG, Gremban KD, et al. VITS-A vision system for autonomous land vehicle navigation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(3): 342-361.
[183] Crisman JD, Thorpe CE. SCARF: a color vision system that tracks roads and intersections[J]. IEEE Transactions on Robotics and Automation, 1993, 9(1): 49-58.
[184] Tremeau A, Borel N. A region growing and merging algorithm to color segmentation[J]. Pattern Recognition, 1997, 30(7): 1191-1203.
[185] Shih FY, Cheng S. Automatic seeded region growing for color image segmentation[J]. Image and Vision Computing, 2005, 23(10): 877-886.
[186]孔俊,王佳男,谷文祥,等.基于区域的自动种子区域生长法的彩色图像分割算法[J].东北师大学报(自然科学版), 2008, 40(4): 47-51.
[187] Cheng HD, Jiang XH, Sun Y, et al. Color image segmentation: advances and prospects[J]. Pattern Recognition, 2001, 34(12): 2259-2281.
[188]陶霖密,徐光佑.机器视觉中的颜色问题及应用[J].科学通报, 2001, 46(3): 178-190.
[189] Schetselaar EM. Fusion by the IHS transform: should we use cylindrical or spherical coordinates[J]. International Journal of Remote Sensing, 1998, 19(4): 759-765.
[190] Van WJ, Gevers T. Robust optical flow from photometric invariants[A]. Proceedings of International Conference on Image Processing[C]. 2004, 3: 1835-1838.
[191] Robin RM.人工智能机器人学导论[M].杜军平,吴立成,胡金春,译.北京:电子工业出版社, 2004.
[192] Umbaugh SE, Moss RH, Stoecker WV. Automatic color segmentation of images with application to detection of variegated coloring in skin tumors[J]. IEEE Engineering in Medicine and Biology Magazine, 1989, 8(4): 43-50.
[193]马建光,施坤林.基于图像相似性测量的移动机器人定位研究[J].探测与控制学报, 2002, 24(4): 51-54.
[194] Studholme C, Hawkes DJ, Hill DL. Normalized entropy measure for multimodality image alignment[A]. Proceedings of SPIE, Medical Imaging 1998: Image Processing[C]. San Diego, CA, USA: 1998, 3338: 132-143.
[195] Studholme C, Hill DL, Hawkes DJ. An overlap invariant entropy measure of 3D medical image alignment[J]. Pattern Recognition, 1999, 32(1): 71-86.
[196] Maes F, Collignon A, Vandermeulen D, et al. Multimodality image registration by maximization of mutual information[J]. Medical Imaging, IEEE Transactions on, 1997, 16(2): 187-198.
[197]曹蹊渺.基于互信息的图像配准算法研究[D].北京交通大学, 2008.
[198]闻芳,周杰,张长水,等.基于局部线性映射神经网络的亮度补偿的彩色人脸检测[J].清华大学学报(自然科学报), 1999, 39(7): 37-40.
[199]梁晓辉,游志胜.自适应的彩色图像光照补偿新方法[J].光电工程, 2006, 33(2): 94-97.
[200] Wettergreen D, Bapna D, Maimone M, et al. Developing Nomad for robotic exploration of the Atacama Desert[J]. ROB AUTOM SYST, 1999, 26(2): 127-148.
[201] Seal JR, Bailey DG, Gupta GS. Depth Perception with a Single Camera[A]. Proceedings of International Conference on Sensing Technology[C]. Palmerston North, New Zealand: 2005: 96-101.
[202]朱志刚,林学訚,石定机,等.基于重投影变换的实时障碍物检测视觉系统[J].计算机研究与发展, 1999, 36(1): 77-84.
[203]熊超,陆起涌,田小芳.基于DirectShow的多线程视频采集系统[J].计算机工程, 2006, (20): 221-223.
[204]陈玥,蔡自兴.基于DirectShow技术的移动机器人视频采集系统[J].华中科技大学学报(自然科学版), 2004, (S1): 87-89.
[205] Zhengyou Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
[206]陈爱华,高诚辉,何炳蔚.计算机视觉中的摄像机标定方法[J].中国工程机械学报, 2006, (4): 498-504.
[207]郑南宁.计算机视觉与模式识别[M].北京:国防工业出版社, 1998.
[208]李庆忠,陈显华,顾伟康.基于彩色立体视觉的障碍物快速检测方法[J].计算机科学, 2003, 30(9): 72-75.
[209]梅园,孙怀江,夏德深.一种基于改进后模板的图像快速细化算法[J].中国图象图形学报, 2006, 11(9): 1306-1311.
[210]白强.基于视觉反馈的智能小车系统研究[D].中南大学, 2007.
[211]赵毅红.可翻转移动机器人运动控制及动力学分析[D].南京航空航天大学, 2008.