基于图像特征点的移动机器人立体视觉SLAM研究
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摘要
移动机器人的同时定位与地图创建(Simultaneous Localization and Mapping, SLAM)问题是整个移动机器人自主导航的基础,也是其真正实现自主化和智能化的最重要条件之一。所谓SLAM就是将移动机器人定位与环境地图创建融为一体,即机器人在运动过程中根据自身位姿估计和传感器对环境的感知构建增量式环境地图,同时利用该地图实现自身的定位。
本文针对室内未知环境下基于图像特征点的移动机器人立体视觉SLAM进行研究。移动机器人在场景中运动,不需要任何先验知识,利用双目立体视觉来感知周围环境信息。并提取稳定的图像特征点来表征3D空间实际物理点,以此作为自然路标,来构建环境的几何地图,同时通过与当前时刻之前所创建的环境地图(自然路标库)中的路标进行匹配,估计机器人当前位姿并更新自然路标库,从而实现移动机器人的SLAM过程。本文着重于以下内容展开研究:图像局部不变特征的提取算法、基于图像特征点的全局定位方法和环境地图表示方法。
为了表征环境中典型的特征信息,本文首先针对图像局部不变特征的提取算法进行研究。而兴趣点作为图像的一种重要局部特征,保留了图像场景中的重要特征信息并同时有效地减少了信息的数据量而且具有旋转不变性,几乎不受光照条件的影响。针对此,本文提出了基于图像二阶多项扩展式局部方向张量的兴趣点检测PLOT(Polynomial Local Orientation Tensor)算子,能够从场景图像中提取尺度、旋转、明亮、仿射等局部不变的兴趣点,来表征机器人当前所在环境的典型局部特征。利用PLOT算子提取图像局部特征不变兴趣点,获得相应的位置和尺度等特征,为了区分这些不变的兴趣点,本文利用SIFT描述符对其进行特征描述。最后通过召回率与准确率图比较了
目前典型的特征提取算子,可知PLOT算子对于各种图像变换表现更稳定。移动机器人SLAM问题同时包括机器人的全局定位和环境地图创建两部分,两部分是密切相联且又相互影响的。移动机器人的全局定位依赖于所创建的环境地图。而基于图像特征点的机器人定位过程就是指将当前帧图像PLOT特征匹配点与路标库中PLOT自然路标对应匹配,获得满足一定约束条件的模型参数,最终得到机器人位姿的最佳估计。本文提出了扩展随机抽样一致性算法来估计机器人的准确位姿,即把机器人的全局定位看作模型参数估计问题,获得机器人在运动平面中的位置和方向角参数。
把获得的图像PLOT特征匹配点当作环境自然路标,提出了基于图像坐标点PLOT特征及对应空间3D坐标等信息来创建环境的几何特征地图。左右摄像机特征点匹配之后,可以获得每一匹配点的特征信息,包括:全局3D坐标、特征尺度、特征方向和描述符等。把观测到的特征点当作环境自然路标,并建立自然路标数据库来记录所获得的路标点。而机器人在环境中运动,不断地获得图像PLOT特征匹配点,故在每帧时刻需要维护更新自然路标数据库,包括:增加自然路标、记录自然路标出现的次数、去除自然路标等,来适合机器人的动态工作环境。
针对基于图像特征点的移动机器人立体视觉SLAM,为了验证本文算法的有效性和准确性,最后基于实际移动机器人平台进行了相关实验研究,并分析了机器人估计位姿及路标数据库中路标的3D坐标误差。本文主要利用卡尔曼滤波和误差传播公式来进行机器人全局位姿和地图路标坐标的误差分析,利用误差协方差矩阵来定量表达其不确定性,并总结了其误差来源及误差抑制方法。
Simultaneous Localization and Mapping (SLAM) is one of the fundamental challenges of autonomous navigation, and also one of essential conditions to fulfill the intelligence for the mobile robot. SLAM is to combine localization and mapping into one procedure, that is to say, mobile robot incrementally builds a consistent map of the environment by its pose estimation and sensing, while simultaneously determining its location within this map.
The paper focuses on the mobile robot visual SLAM, which is based on the image features of the indoor unknown environment. Without any prior knowledge, the robot obtains image of environment based on the binocular stereo vision, and extracts the image features as the natural landmarks. Then the feature-based geometry map is built, and simultaneously robot localization is fulfilled by the current pose estimate based on the matches between the image features and the landmarks in the prebuilt database. This dissertation does some in-depth studies on the following sub-problems in the visual SLAM: image local invariant features extracting algorithm, global localization and environment mapping based on the image features.
In order to represent the distinctive information in the scene, the image local invariant features extracting algorithm is studied. As an important local feature, interest point preserves typical information and reduces the computation efficiently. Moreover it is invariant to the rotation transformation and almost not affected by illumination changes. The paper proposes a novel method for detecting scale, rotation, illumination, and affine invariant interest points, coined PLOT (Polynomial Local Orientation Tensor). PLOT is based on the local orientation tensor, which is constructed from the second order polynomial expansion of the image signal. The corresponding coordinates and scales features are obtained after extracting the PLOT interest points. The paper also uses the SIFT descriptor to describe its feature. PLOT shows strong performance based on the evaluation criterion using the recall vs. 1-precision graphs.
Global localization and mapping are taken as two-in-one procedure in the robot SLAM. They interact and support each other. Accurate global localization relies on accurate map. The procedure of global localization is to obtain mobile robot current 3D coordinate and direction in the global coordinate system. It is based on the PLOT features to obtain a current best pose estimate by matching the PLOT features in the current frame with the PLOT landmarks in the built database under some constraints. Treating the global localization as model parameters estimation problem, the paper proposes a global localization algorithm by using extended RANSAC to obtain accurate position and orientation for mobile robot in its movement plane.
The geometry map is built based on the 3D coordinates and other information of the PLOT features, such as orientations, scales, and so on. Once PLOT features are matched, their features are also obtained, such as 3D coordinates in the camera system, scales, orientations, descriptors and so on. They can be taken as the natural landmarks in the scene, and used to build the landmark database. As the robot moves in the environment, the database is updated every frame, including adding a new landmark, incrementing miss count or accumulative count, pruning a invalidate landmarks, etc.
Experiments of mobile robot stereo visual SLAM based on the image PLOT features are implemented to verify the validity and accuracy of the proposed algorithm. Then the error analysis in mobile robot SLAM based on the PLOT features is discussed. The paper analyze the errors of the robot pose estimate and landmark 3D coordinate in the map using Kalman filter and error propagation formulae. The covariance error matrix is used to express their uncertainty. In the end the error sources and their reducing methods are discussed.
本文针对室内未知环境下基于图像特征点的移动机器人立体视觉SLAM进行研究。移动机器人在场景中运动,不需要任何先验知识,利用双目立体视觉来感知周围环境信息。并提取稳定的图像特征点来表征3D空间实际物理点,以此作为自然路标,来构建环境的几何地图,同时通过与当前时刻之前所创建的环境地图(自然路标库)中的路标进行匹配,估计机器人当前位姿并更新自然路标库,从而实现移动机器人的SLAM过程。本文着重于以下内容展开研究:图像局部不变特征的提取算法、基于图像特征点的全局定位方法和环境地图表示方法。
为了表征环境中典型的特征信息,本文首先针对图像局部不变特征的提取算法进行研究。而兴趣点作为图像的一种重要局部特征,保留了图像场景中的重要特征信息并同时有效地减少了信息的数据量而且具有旋转不变性,几乎不受光照条件的影响。针对此,本文提出了基于图像二阶多项扩展式局部方向张量的兴趣点检测PLOT(Polynomial Local Orientation Tensor)算子,能够从场景图像中提取尺度、旋转、明亮、仿射等局部不变的兴趣点,来表征机器人当前所在环境的典型局部特征。利用PLOT算子提取图像局部特征不变兴趣点,获得相应的位置和尺度等特征,为了区分这些不变的兴趣点,本文利用SIFT描述符对其进行特征描述。最后通过召回率与准确率图比较了
目前典型的特征提取算子,可知PLOT算子对于各种图像变换表现更稳定。移动机器人SLAM问题同时包括机器人的全局定位和环境地图创建两部分,两部分是密切相联且又相互影响的。移动机器人的全局定位依赖于所创建的环境地图。而基于图像特征点的机器人定位过程就是指将当前帧图像PLOT特征匹配点与路标库中PLOT自然路标对应匹配,获得满足一定约束条件的模型参数,最终得到机器人位姿的最佳估计。本文提出了扩展随机抽样一致性算法来估计机器人的准确位姿,即把机器人的全局定位看作模型参数估计问题,获得机器人在运动平面中的位置和方向角参数。
把获得的图像PLOT特征匹配点当作环境自然路标,提出了基于图像坐标点PLOT特征及对应空间3D坐标等信息来创建环境的几何特征地图。左右摄像机特征点匹配之后,可以获得每一匹配点的特征信息,包括:全局3D坐标、特征尺度、特征方向和描述符等。把观测到的特征点当作环境自然路标,并建立自然路标数据库来记录所获得的路标点。而机器人在环境中运动,不断地获得图像PLOT特征匹配点,故在每帧时刻需要维护更新自然路标数据库,包括:增加自然路标、记录自然路标出现的次数、去除自然路标等,来适合机器人的动态工作环境。
针对基于图像特征点的移动机器人立体视觉SLAM,为了验证本文算法的有效性和准确性,最后基于实际移动机器人平台进行了相关实验研究,并分析了机器人估计位姿及路标数据库中路标的3D坐标误差。本文主要利用卡尔曼滤波和误差传播公式来进行机器人全局位姿和地图路标坐标的误差分析,利用误差协方差矩阵来定量表达其不确定性,并总结了其误差来源及误差抑制方法。
Simultaneous Localization and Mapping (SLAM) is one of the fundamental challenges of autonomous navigation, and also one of essential conditions to fulfill the intelligence for the mobile robot. SLAM is to combine localization and mapping into one procedure, that is to say, mobile robot incrementally builds a consistent map of the environment by its pose estimation and sensing, while simultaneously determining its location within this map.
The paper focuses on the mobile robot visual SLAM, which is based on the image features of the indoor unknown environment. Without any prior knowledge, the robot obtains image of environment based on the binocular stereo vision, and extracts the image features as the natural landmarks. Then the feature-based geometry map is built, and simultaneously robot localization is fulfilled by the current pose estimate based on the matches between the image features and the landmarks in the prebuilt database. This dissertation does some in-depth studies on the following sub-problems in the visual SLAM: image local invariant features extracting algorithm, global localization and environment mapping based on the image features.
In order to represent the distinctive information in the scene, the image local invariant features extracting algorithm is studied. As an important local feature, interest point preserves typical information and reduces the computation efficiently. Moreover it is invariant to the rotation transformation and almost not affected by illumination changes. The paper proposes a novel method for detecting scale, rotation, illumination, and affine invariant interest points, coined PLOT (Polynomial Local Orientation Tensor). PLOT is based on the local orientation tensor, which is constructed from the second order polynomial expansion of the image signal. The corresponding coordinates and scales features are obtained after extracting the PLOT interest points. The paper also uses the SIFT descriptor to describe its feature. PLOT shows strong performance based on the evaluation criterion using the recall vs. 1-precision graphs.
Global localization and mapping are taken as two-in-one procedure in the robot SLAM. They interact and support each other. Accurate global localization relies on accurate map. The procedure of global localization is to obtain mobile robot current 3D coordinate and direction in the global coordinate system. It is based on the PLOT features to obtain a current best pose estimate by matching the PLOT features in the current frame with the PLOT landmarks in the built database under some constraints. Treating the global localization as model parameters estimation problem, the paper proposes a global localization algorithm by using extended RANSAC to obtain accurate position and orientation for mobile robot in its movement plane.
The geometry map is built based on the 3D coordinates and other information of the PLOT features, such as orientations, scales, and so on. Once PLOT features are matched, their features are also obtained, such as 3D coordinates in the camera system, scales, orientations, descriptors and so on. They can be taken as the natural landmarks in the scene, and used to build the landmark database. As the robot moves in the environment, the database is updated every frame, including adding a new landmark, incrementing miss count or accumulative count, pruning a invalidate landmarks, etc.
Experiments of mobile robot stereo visual SLAM based on the image PLOT features are implemented to verify the validity and accuracy of the proposed algorithm. Then the error analysis in mobile robot SLAM based on the PLOT features is discussed. The paper analyze the errors of the robot pose estimate and landmark 3D coordinate in the map using Kalman filter and error propagation formulae. The covariance error matrix is used to express their uncertainty. In the end the error sources and their reducing methods are discussed.
引文
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79 K. Mikolajczyk, C. Schmid. Indexing based on scale invariant interest points. Proceedings of the 8th IEEE International Conference on Computer Vision. 2001, 1: 525-531
80 H. Knutsson. Representing local structure using tensors. The 6th Scandinavian Conference on Image Analysis. 1989: 244-251
81 S. Aja-Fernández, R. de L. Garcia, D. Tao, et al. Tensors in image processing and computer vision. London: Springer. 2009: 59-78
82 B. Svensson, A. Brun, M. Andersson, et al. Estimation of non-Cartesian local structure tensor fields. Proceedings of the 15th Scandinavian conference on image analysis. 2007: 948-957
83 U. K?the. Edge and junction detection with an improved structure tensor. Proceeding of 25th DAGM Symposium on Pattern Recognition. 2003: 25-32
84 G. H. Granlund, H. Knutsson. Signal processing for computer vision. Dordrecht: Kluwer Academic Publishers. 1995
85 U. K?the. Low-level feature detection using the boundary tensor. Visualization and Processing of Tensor Fields, Series on Mathematics and Visualization. 2006, (I): 63-79
86 B. Johansson, G. Farneb?ck. A theoretical comparison of different orientation tensors. Proceedings SSAB02 Symposium on Image Analysis. 2002: 69-73
87 K. Nordberg, G. Farneb?ck. Estimation of orientation tensors for simple signals by means of second-order filters. Signal Processing: Image Communication. 2005, 20(6): 582-594
88 G. Farneb?ck. Polynomial expansion for orientation and motion estimation. PhD thesis, Link?ping University. 2002: 69-82
89郭剑辉,赵春霞.一种新的粒子滤波SLAM算法.计算机研究与发展.2008, 45(5): 853-860
90 D. Marr. Vision. San Francisco: W. H. Freeman and Company. 1982
91刘孝艳.扩散张量在图像处理中的应用.西安电子科技大学硕士学位论文. 2008
92 J. Bigun, G. Granlund. Optimal orientation detection of linear symmetry. Proceedings of the First International Conference on Computer Vision. 1987: 433-438
93 A. Baumberg. Reliable feature matching across widely separated views. IEEE Conference on Computer Vision and Pattern Recognition. 2000, 1: 774-781
94 K. Mikolajczyk, C. Schmid. An affine invariant interest point detector. Proceedings of the 7th European Conference on Computer Vision. 2002: 128-142
95 K. Mikolajczyk, T. Tuytelaars, C. Schmid, et al. A comparison of affine region detectors. International Journal of Computer Vision. 2005, 65(1/2):
43-72
96 K. Mikolajczyk. Affine Covariant Features. http://www.robots.ox.ac.uk/~vgg /research/affine/. 2007
97 K. Mikolajczyk, C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005, 27(10): 1615-1630
98 J. J. Koenderink, A. J. van Doorn. Representation of local geometry in the visual system. Biological Cybernetics. 1987, 55: 367-375
99 W. T. Freeman, E. H. Adelson. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991, 13(9): 891-906
100 A. Baumberg. Reliable feature matching across widely separated views. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2000: 774-781
101 K. Mikolajczyk. Detection of local features invariant to affine transformations. PhD thesis, Institute National Polytechnique de Grenoble. 2002
102 M. Hu. Visual pattern recognition by moment invariants. IEEE Transactionson Information Theory, 1962, 8(2): 179-187
103 J. Flusser, T. Suk. Affine moment invariants: a new tool for character recognition. Pattern Recognition Letters. 1994, 15: 433-436
104 S. X. Liao, M. Pawlak. On the accuracy of Zernike moments for image analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998, 12(5): 1358-1364
105 S. Lazebnik, C. Schmid, J. Ponce. Sparse texture representation using affine-invariant neighborhoods. Proceeding Conference Computer Vision and Pattern Recognition. 2003: 319-324
106 S. Belongie, J. Malik, J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002, 2(4): 509-522
107石杏喜,赵春霞,郭剑辉.基于PF/CUKF/EKF的移动机器人SLAM框架算法.电子学报. 2009, 37(8): 1865-1868
108厉茂海,洪炳熔,罗荣华.移动机器人的同时定位和地图创建方法.哈尔滨工业大学学报, 2004, 36(7): 874-876
109杨恒,王庆.一种新的局部不变特征检测和描述算法.计算机学报. 2010, 33(5): 935-944
110 Y. Ke, R. Sukthankar. PCA-SIFT: a more distinctive representation for local image descriptors. Proceeding of Conference on Vision and Pattern Recognition. 2004: 511-517
111 D. Gabor. Theory of communication. J. IEE. 1946, 93(26): 429-457
112 L. V. Gool, T. Moons, D. Ungureanu. Affine/photometric invariants for planar intensity patterns. Proceeding of the Fourth European Conference Computer Vision. 1996: 642-651
113祝继华,郑南宁,袁泽剑等.基于中心差分粒子滤波的SLAM算法.自动化学报. 2010, 36(2): 249-257
114王璐.未知环境中移动机器人视觉环境建模与定位研究.中南大学博士学位论文. 2007
115 D. Lowe. Demo software: SIFT keypoint detector. http://www.cs.ubc.ca/ ~lowe/keypoints/. 2005
116 Computer Vision Laboratory ETH. SURF: Speeded Up Robust Features. http://www.vision.ee.ethz.ch/~surf/. 2008
117 M. A. Fischler, R. C. Bolles. Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM. 1981, 24(6): 381-395
118 L. Xu, E. Oja. Randomized Hough Transform (RHT): basic mechanism, algorithms, and computational complexities. CVGIP: Image Understanding. 1993, 57(2): 131-154
119 D. G. Lowe. Fitting parameterized three-dimensional models to images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991, 13(5): 441-450
120 P. R. Bevington, D. K. Robinson. Data reduction and error analysis for the physical sciences. New York: McGrawHill, second edition. 1992
121 T. Kadir, A. Zisserman, M. Brady. An affine invariant salient region detector. Proceedings of the 8th European Conference on Computer Vision. 2004: 345-457
122张少辉,沈晓蓉,范耀祖.一种基于图像特征点提取及匹配的方法.北京航空航天大学学报. 2008, 34(5): 516-519
123 B. Florack, J. Koenderink, M. Viergever. General intensity transformations and differential invariants. Journal of Mathematical Imaging and Vision. 1994, 4(2): 171-187
124 J. Neira, J. D. Tardos. Data association in stochastic mapping using the joint compatibility test. IEEE Transactions on Robotics and Automation. 2001, 17(6): 890-897
125季秀才,郑志强,张辉. SLAM问题中机器人定位误差分析与控制.自动化学报. 2008, 34(3): 323-330
126 S. Thrun. Robotic mapping: a survey. Exploring artificial intelligence in the new millennium. San Francisco: Morgan Kaufmann Publishers Incorporated. 2003
127 Y. Lee, T. Kwon, J. Song. SLAM of a mobile robot using thinning-based topological information. International Journal of Control, Automation, and Systems. 2007, 5(5): 577-583
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77 T. Lindeberg, J. Garding. Shape-adapted smoothing in estimation of 3-D shape cues from affine distortions of local 2-D brightness structure. Image and Vision Computing. 1997, 15(6): 415-434
78 T. Lindeberg. Feature detection with automatic scale selection. International Journal of Computer Vision. 1998, 30(2): 79-116
79 K. Mikolajczyk, C. Schmid. Indexing based on scale invariant interest points. Proceedings of the 8th IEEE International Conference on Computer Vision. 2001, 1: 525-531
80 H. Knutsson. Representing local structure using tensors. The 6th Scandinavian Conference on Image Analysis. 1989: 244-251
81 S. Aja-Fernández, R. de L. Garcia, D. Tao, et al. Tensors in image processing and computer vision. London: Springer. 2009: 59-78
82 B. Svensson, A. Brun, M. Andersson, et al. Estimation of non-Cartesian local structure tensor fields. Proceedings of the 15th Scandinavian conference on image analysis. 2007: 948-957
83 U. K?the. Edge and junction detection with an improved structure tensor. Proceeding of 25th DAGM Symposium on Pattern Recognition. 2003: 25-32
84 G. H. Granlund, H. Knutsson. Signal processing for computer vision. Dordrecht: Kluwer Academic Publishers. 1995
85 U. K?the. Low-level feature detection using the boundary tensor. Visualization and Processing of Tensor Fields, Series on Mathematics and Visualization. 2006, (I): 63-79
86 B. Johansson, G. Farneb?ck. A theoretical comparison of different orientation tensors. Proceedings SSAB02 Symposium on Image Analysis. 2002: 69-73
87 K. Nordberg, G. Farneb?ck. Estimation of orientation tensors for simple signals by means of second-order filters. Signal Processing: Image Communication. 2005, 20(6): 582-594
88 G. Farneb?ck. Polynomial expansion for orientation and motion estimation. PhD thesis, Link?ping University. 2002: 69-82
89郭剑辉,赵春霞.一种新的粒子滤波SLAM算法.计算机研究与发展.2008, 45(5): 853-860
90 D. Marr. Vision. San Francisco: W. H. Freeman and Company. 1982
91刘孝艳.扩散张量在图像处理中的应用.西安电子科技大学硕士学位论文. 2008
92 J. Bigun, G. Granlund. Optimal orientation detection of linear symmetry. Proceedings of the First International Conference on Computer Vision. 1987: 433-438
93 A. Baumberg. Reliable feature matching across widely separated views. IEEE Conference on Computer Vision and Pattern Recognition. 2000, 1: 774-781
94 K. Mikolajczyk, C. Schmid. An affine invariant interest point detector. Proceedings of the 7th European Conference on Computer Vision. 2002: 128-142
95 K. Mikolajczyk, T. Tuytelaars, C. Schmid, et al. A comparison of affine region detectors. International Journal of Computer Vision. 2005, 65(1/2):
43-72
96 K. Mikolajczyk. Affine Covariant Features. http://www.robots.ox.ac.uk/~vgg /research/affine/. 2007
97 K. Mikolajczyk, C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005, 27(10): 1615-1630
98 J. J. Koenderink, A. J. van Doorn. Representation of local geometry in the visual system. Biological Cybernetics. 1987, 55: 367-375
99 W. T. Freeman, E. H. Adelson. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991, 13(9): 891-906
100 A. Baumberg. Reliable feature matching across widely separated views. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2000: 774-781
101 K. Mikolajczyk. Detection of local features invariant to affine transformations. PhD thesis, Institute National Polytechnique de Grenoble. 2002
102 M. Hu. Visual pattern recognition by moment invariants. IEEE Transactionson Information Theory, 1962, 8(2): 179-187
103 J. Flusser, T. Suk. Affine moment invariants: a new tool for character recognition. Pattern Recognition Letters. 1994, 15: 433-436
104 S. X. Liao, M. Pawlak. On the accuracy of Zernike moments for image analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998, 12(5): 1358-1364
105 S. Lazebnik, C. Schmid, J. Ponce. Sparse texture representation using affine-invariant neighborhoods. Proceeding Conference Computer Vision and Pattern Recognition. 2003: 319-324
106 S. Belongie, J. Malik, J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002, 2(4): 509-522
107石杏喜,赵春霞,郭剑辉.基于PF/CUKF/EKF的移动机器人SLAM框架算法.电子学报. 2009, 37(8): 1865-1868
108厉茂海,洪炳熔,罗荣华.移动机器人的同时定位和地图创建方法.哈尔滨工业大学学报, 2004, 36(7): 874-876
109杨恒,王庆.一种新的局部不变特征检测和描述算法.计算机学报. 2010, 33(5): 935-944
110 Y. Ke, R. Sukthankar. PCA-SIFT: a more distinctive representation for local image descriptors. Proceeding of Conference on Vision and Pattern Recognition. 2004: 511-517
111 D. Gabor. Theory of communication. J. IEE. 1946, 93(26): 429-457
112 L. V. Gool, T. Moons, D. Ungureanu. Affine/photometric invariants for planar intensity patterns. Proceeding of the Fourth European Conference Computer Vision. 1996: 642-651
113祝继华,郑南宁,袁泽剑等.基于中心差分粒子滤波的SLAM算法.自动化学报. 2010, 36(2): 249-257
114王璐.未知环境中移动机器人视觉环境建模与定位研究.中南大学博士学位论文. 2007
115 D. Lowe. Demo software: SIFT keypoint detector. http://www.cs.ubc.ca/ ~lowe/keypoints/. 2005
116 Computer Vision Laboratory ETH. SURF: Speeded Up Robust Features. http://www.vision.ee.ethz.ch/~surf/. 2008
117 M. A. Fischler, R. C. Bolles. Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM. 1981, 24(6): 381-395
118 L. Xu, E. Oja. Randomized Hough Transform (RHT): basic mechanism, algorithms, and computational complexities. CVGIP: Image Understanding. 1993, 57(2): 131-154
119 D. G. Lowe. Fitting parameterized three-dimensional models to images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1991, 13(5): 441-450
120 P. R. Bevington, D. K. Robinson. Data reduction and error analysis for the physical sciences. New York: McGrawHill, second edition. 1992
121 T. Kadir, A. Zisserman, M. Brady. An affine invariant salient region detector. Proceedings of the 8th European Conference on Computer Vision. 2004: 345-457
122张少辉,沈晓蓉,范耀祖.一种基于图像特征点提取及匹配的方法.北京航空航天大学学报. 2008, 34(5): 516-519
123 B. Florack, J. Koenderink, M. Viergever. General intensity transformations and differential invariants. Journal of Mathematical Imaging and Vision. 1994, 4(2): 171-187
124 J. Neira, J. D. Tardos. Data association in stochastic mapping using the joint compatibility test. IEEE Transactions on Robotics and Automation. 2001, 17(6): 890-897
125季秀才,郑志强,张辉. SLAM问题中机器人定位误差分析与控制.自动化学报. 2008, 34(3): 323-330
126 S. Thrun. Robotic mapping: a survey. Exploring artificial intelligence in the new millennium. San Francisco: Morgan Kaufmann Publishers Incorporated. 2003
127 Y. Lee, T. Kwon, J. Song. SLAM of a mobile robot using thinning-based topological information. International Journal of Control, Automation, and Systems. 2007, 5(5): 577-583
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