基于全景视觉的移动机器人SLAM方法研究
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摘要
在未知的环境中,如果机器人只利用里程计预测自身所处的位置,随着时间的积累误差会越来越大。因此,为了给机器人精确的定位需要用激光传感器或者视觉传感器等外部传感器从周围环境中提取路标,利用这些路标创建出周围环境的地图,利用地图信息来修正机器人的位置。为了得到机器人的精确位置需要利用环境地图对里程计数据进行修正,为了创建精确的环境地图机器人要知道自身的精确位置,这类问题称为同时定位与地图创建问题,其中包括机器人定位、特征提取和地图创建等技术,需要在准确性、鲁棒性和实时性三个方面取得突破。
由于视觉传感器具有信息量丰富,采样周期短等优点,近年来在移动机器人的导航领域得到了广泛的应用。目前基于视觉的同时定位与地图创建技术主要采用的是普通视觉传感器,但是,普通视觉传感器视野范围狭窄,仅能观测前向60°的信息,对视觉路标的连续观测和跟踪能力有限。全景视觉传感器具有全向360°的感知范围,视觉路标可以再其视野范围内停留更长的时间,增强了视觉传感器对路标的连续观测和跟踪能力。本文对基于全景视觉的移动机器人同时定位与地图创建方法进行了研究。
首先,建立了基于全景视觉的移动机器人SLAM系统感知模型,依据全景视觉系统的成像原理得到视觉路标在地面的投影位置,根据基于视差原理的双目立体视觉三维测量方法和机器人在两幅全景图像中的位姿,从而获得了视觉路标在全局坐标系中的位置。
其次,对特征匹配条件作了改进。原匹配算法在特征匹配过程中存在着大量的错误匹配,因此对原算法作了如下改进：1)如果匹配结果中存在的两个以上的特征点与另一幅图像中的同一个特征点相匹配的的情况,则比较它们的特征描述符的欧氏距离,保留欧氏距离最小的匹配点对,删除其他匹配点对；2)对于其他误匹配情况则采用角度限制和长度限制的方法来去除。即把匹配成功的特征点在两帧图像的坐标系中的角度变化的绝对值与所有匹配成功的特征点的角度变化的绝对值的平均值进行比较,如果相差较大则认为匹配有误,删除匹配点；求出所有匹配成功的特征点之间的欧氏距离的平均值,将与平均值差距较大的匹配点对删除。实验结果表明改进后的算法提高了匹配的准确性,消除了错误匹配对SLAM的影响,增强了SLAM系统的鲁棒性。
再次,将基于全景视觉的特征提取方法与EKF和FastSLAM算法相结合,提出了基于全景视觉的SLAM算法,该算法利用改进的SURF算法提取视觉路标,根据系统的观测模型定位出路标位置,进而通过EKF或FastSLAM算法同时更新机器人位置和地图信息,仿真实验结果证明了全景视觉相对于普通视觉的优越性,通过真实的机器人试验证明了本文所设计的算法的可行性。
最后,创建了基于SURF算法的特征地图库。地图库中的特征随着时间的推移会越来越多,把图像的特征点与地图库中的所有特征点进行匹配需要大量的时间,将给实时特征匹配带来困难,甚至计算灾难问题。因此本文的特征地图库有许多子地图构成,通过一个代价函数选择与当前全景图像进行匹配的子地图,以保证机器人不但能够得到充足的视觉路标,而且能够增强SLAM的实时性。
If the robot only uses odometer to predict the location of itself in unknown environments, error will be more and more larger as time goes on. Therefore, In order to accurately localize the robot, we need to use vision sensors, laser sensors or external sensors to extract landmarks from the surrounding environments, build a map based on these landmarks, use the map information to update the location of the robot. In order to get the exact location of the robot, we need to use the map of the environment to update odometer data of the robot.In order to build an accurate map of the environment the robot should know its exact location, such problem called simultaneous localization and mapping problem, including robot localization、feature extraction and map building techniques, need breakthrough in three aspects:real time, robustness and accuracy.
Because vision sensor has the advantages of abundant information and short sampling period, have been widely applied to mobile robot navigation in recent years. Nowadays, vision-based simultaneous localization and mapping technology is mainly based common vision sensors. However, the observation view of common vision sensors is narrow, it can only observe60°range of environmental information in the forward direction, continuous observation and tracking capabilities on visual landmark are limited. Omni-vision sensor has a sensing range of360°, Visual landmark can stay longer within its field of view, so, enhanced the ability of continuous observation and tracking on visual landmarks. In this paper, Omni-vision based simultaneous localization and mapping method for mobile robot is studied.
First, SLAM perception model of mobile robot is established. According to the imaging principle of Omni-vision system, the projection location of the visual landmarks on the ground is obtained. According to the three-dimensional measurement method of binocular stereo vision based on principle of parallax and robot pose in the two omnidirectional images, visual landmark location in the global coordinate system is obtained.
Second, feature matching guidelines are improved. The original matching algorithm has a large number of error matches in feature matching process, therefore the original algorithm is improved as follows:1) If there are two or more feature points extracted from one omnidirectional image matched with the same feature point extracted from another omnidirectional image, compare the euclidean distances of feature descriptors, keep the matching points which have minimum euclidean distance, delete the other matching points;2) angular limitation and length limitation are used to remove mismatches of other conditions, that is compare absolute value of angle change of successful matching feature points in two image coordinate system with the average absolute change in angle of all matched feature points, If the absolute angle change of two matching feature points has larger difference with the average value, remove the two matching feature points; calculate the average euclidean distance of all successful matching feature points. Remove matching points which has larger difference with the average value. Experimental results show that the improved algorithm improved the matching accuracy.eliminated mismatching influence on SLAM., enhanced robustness of the SLAM system.
Third, Omni-vision based feature extraction method combined with the EKF and FastSLAM algorithm, omni-vision based simultaneous localization and mapping algorithm is introduced. The presented method uses improved SURF algorithm to extract visual landmarks, According to the observation model localize the position of the landmarks, and then updates the robot position and map information by EKF or FastSLAM algorithm. The simulation experimental results show the superiority of omni-vision sensor relative to the common vision sensor. The feasibility of the algorithm is proved by the real robot experiment designed in this paper.
Finally, established feature map database, there are more features in map database as time goes on. To match feature points of the image with feature map database will need a lot of time., this will create difficulties for real-time feature matching, or even bring computing Disaster. Therefore, feature map database structure by sub-maps, select a sub-map through a cost function to match with the current panoramic image. To ensure that the robot can not only get enough visual landmarks, but also to enhance the real-time of SLAM.
由于视觉传感器具有信息量丰富,采样周期短等优点,近年来在移动机器人的导航领域得到了广泛的应用。目前基于视觉的同时定位与地图创建技术主要采用的是普通视觉传感器,但是,普通视觉传感器视野范围狭窄,仅能观测前向60°的信息,对视觉路标的连续观测和跟踪能力有限。全景视觉传感器具有全向360°的感知范围,视觉路标可以再其视野范围内停留更长的时间,增强了视觉传感器对路标的连续观测和跟踪能力。本文对基于全景视觉的移动机器人同时定位与地图创建方法进行了研究。
首先,建立了基于全景视觉的移动机器人SLAM系统感知模型,依据全景视觉系统的成像原理得到视觉路标在地面的投影位置,根据基于视差原理的双目立体视觉三维测量方法和机器人在两幅全景图像中的位姿,从而获得了视觉路标在全局坐标系中的位置。
其次,对特征匹配条件作了改进。原匹配算法在特征匹配过程中存在着大量的错误匹配,因此对原算法作了如下改进：1)如果匹配结果中存在的两个以上的特征点与另一幅图像中的同一个特征点相匹配的的情况,则比较它们的特征描述符的欧氏距离,保留欧氏距离最小的匹配点对,删除其他匹配点对；2)对于其他误匹配情况则采用角度限制和长度限制的方法来去除。即把匹配成功的特征点在两帧图像的坐标系中的角度变化的绝对值与所有匹配成功的特征点的角度变化的绝对值的平均值进行比较,如果相差较大则认为匹配有误,删除匹配点；求出所有匹配成功的特征点之间的欧氏距离的平均值,将与平均值差距较大的匹配点对删除。实验结果表明改进后的算法提高了匹配的准确性,消除了错误匹配对SLAM的影响,增强了SLAM系统的鲁棒性。
再次,将基于全景视觉的特征提取方法与EKF和FastSLAM算法相结合,提出了基于全景视觉的SLAM算法,该算法利用改进的SURF算法提取视觉路标,根据系统的观测模型定位出路标位置,进而通过EKF或FastSLAM算法同时更新机器人位置和地图信息,仿真实验结果证明了全景视觉相对于普通视觉的优越性,通过真实的机器人试验证明了本文所设计的算法的可行性。
最后,创建了基于SURF算法的特征地图库。地图库中的特征随着时间的推移会越来越多,把图像的特征点与地图库中的所有特征点进行匹配需要大量的时间,将给实时特征匹配带来困难,甚至计算灾难问题。因此本文的特征地图库有许多子地图构成,通过一个代价函数选择与当前全景图像进行匹配的子地图,以保证机器人不但能够得到充足的视觉路标,而且能够增强SLAM的实时性。
If the robot only uses odometer to predict the location of itself in unknown environments, error will be more and more larger as time goes on. Therefore, In order to accurately localize the robot, we need to use vision sensors, laser sensors or external sensors to extract landmarks from the surrounding environments, build a map based on these landmarks, use the map information to update the location of the robot. In order to get the exact location of the robot, we need to use the map of the environment to update odometer data of the robot.In order to build an accurate map of the environment the robot should know its exact location, such problem called simultaneous localization and mapping problem, including robot localization、feature extraction and map building techniques, need breakthrough in three aspects:real time, robustness and accuracy.
Because vision sensor has the advantages of abundant information and short sampling period, have been widely applied to mobile robot navigation in recent years. Nowadays, vision-based simultaneous localization and mapping technology is mainly based common vision sensors. However, the observation view of common vision sensors is narrow, it can only observe60°range of environmental information in the forward direction, continuous observation and tracking capabilities on visual landmark are limited. Omni-vision sensor has a sensing range of360°, Visual landmark can stay longer within its field of view, so, enhanced the ability of continuous observation and tracking on visual landmarks. In this paper, Omni-vision based simultaneous localization and mapping method for mobile robot is studied.
First, SLAM perception model of mobile robot is established. According to the imaging principle of Omni-vision system, the projection location of the visual landmarks on the ground is obtained. According to the three-dimensional measurement method of binocular stereo vision based on principle of parallax and robot pose in the two omnidirectional images, visual landmark location in the global coordinate system is obtained.
Second, feature matching guidelines are improved. The original matching algorithm has a large number of error matches in feature matching process, therefore the original algorithm is improved as follows:1) If there are two or more feature points extracted from one omnidirectional image matched with the same feature point extracted from another omnidirectional image, compare the euclidean distances of feature descriptors, keep the matching points which have minimum euclidean distance, delete the other matching points;2) angular limitation and length limitation are used to remove mismatches of other conditions, that is compare absolute value of angle change of successful matching feature points in two image coordinate system with the average absolute change in angle of all matched feature points, If the absolute angle change of two matching feature points has larger difference with the average value, remove the two matching feature points; calculate the average euclidean distance of all successful matching feature points. Remove matching points which has larger difference with the average value. Experimental results show that the improved algorithm improved the matching accuracy.eliminated mismatching influence on SLAM., enhanced robustness of the SLAM system.
Third, Omni-vision based feature extraction method combined with the EKF and FastSLAM algorithm, omni-vision based simultaneous localization and mapping algorithm is introduced. The presented method uses improved SURF algorithm to extract visual landmarks, According to the observation model localize the position of the landmarks, and then updates the robot position and map information by EKF or FastSLAM algorithm. The simulation experimental results show the superiority of omni-vision sensor relative to the common vision sensor. The feasibility of the algorithm is proved by the real robot experiment designed in this paper.
Finally, established feature map database, there are more features in map database as time goes on. To match feature points of the image with feature map database will need a lot of time., this will create difficulties for real-time feature matching, or even bring computing Disaster. Therefore, feature map database structure by sub-maps, select a sub-map through a cost function to match with the current panoramic image. To ensure that the robot can not only get enough visual landmarks, but also to enhance the real-time of SLAM.
引文
[1]蔡自兴,贺汉根,陈虹.未知环境中移动机器人导控制研究的若干问题.控制与策.2002,17(4)：385-39页
[2]西格沃特,诺巴克什著.李人厚译.自主移动机器人导论.西安：西安交通大学出版社,2004
[3]史美萍.基于人机协同的月亮车路径规划技术研究.国防科学技术大学博士论文.2006
[4]陈晓东等编著.警用机器人.北京：科学出版社,2008
[5]张宏烈.移动机器人全局路径规划的研究.哈尔滨工程大学硕士论文,2002
[6]项志宇.基于激光雷达的移动机器人障碍物检测和自定位.浙江大学博士论文,2002
[7]L. Ojeda, G. Reina, J. Borenstein. Experimental Results from FLEXnav:An Expert Rule-based Dead-reckoning System for Mars Rovers. Porc. of IEEE Aerospace Conference.2004:816-825P
[8]贾一.美国的火星探测机器人.机器人技术与应用.2001,5：1-7页
[9]Gueaieb W, Miah M. S.An intelligent mobile robot navigation technique using RFID technology.IEEE Transactions on Instrumentation and Measurement.2008,57(9): 1908-1917P
[10]Morales Y, Carballo A, Takeuchi E, Aburadani A, Tsubouchi T. Autonomous robot navigation in outdoor cluttered pedestrian walkways. Journal of Field Robotics,2009, 26(8):609-635P
[11]Betke M, Gurvits L. Mobile robot localization using landmarks. IEEE Trnasaction on Robotics and Automation.1997,13(2):251-263P
[12]王卫华,陈卫华,席裕庚.基于不确定信息的移动机器人地图创建研究进展.机器人.2001,23(6)：563-56P
[13]Castellnaos J A, Neira J, Struass O, et al. Detecting high level features for mobile robot localization. Porc. of IEEE Int Conefrence on Multisensor Fusion and Integration for Intelligent Systems.1996:611-618P
[14]梁志伟,马旭东,戴先中等.基于分布式感知的移动机器人同时定位与地图创建.机器人.2009,31(1)：33-39P
[15]J. J. Leanard, H. F. Durrant-Whyte. Mobile robot localization by tracking geometric beacons. IEEE Transaction on Robotics and Automation.1991,7(3):376-382P
[16]Borenstein J, Feng L.UMBmark-a method for measuring, comparing, and correcting dead-reckoning errors in mobile robots. Technical Report UM-MEAM-94-22, University of Michigan,1994
[17]Borenstein J, Feng L. Measurement and correction of systematic odometry errors inmobile robots. IEEE Transactions on Robotics and Automation.1996,12(6): 869-880P
[18]王玉全.基于全景视觉的移动机器人同时定位与地图创建方法研究.哈尔滨工程大学博士论文,2010
[19]Stephen Se, David G. Lowe and James J. Little.Vision-based global localization and mapping for mobile robots. IEEE Transactions on Robotics.2005,21(3):364-375P
[20]许俊勇.移动机器人全景vSLAM研究.上海交通大学硕士论文,2008
[21]Chatila R, Laumond J P. and consistent world modeling for mobile robots Position referencing and and consistent world modeling for mobile robots. EEE Int Conefrence on Robotics and automation.1985:138-145P
[22]Kuipers B, Byun Y T. Robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Journal of Robotics and Autonomous Systems. 1991,8(1-2):47-63P
[23]Avots D, Lim E, Thibaux R, et al. A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments. Porc. of IEEE/RSJ International Conference Inteligent Robots and System.2002,1: 521-526P
[24]Hahnel D, Schulz D, Burgard W. Map building with mobile robots in populated environments. Advanced Robotics,2003,17(7):579-598P
[25]Thrun S, Fox D, Burgard W. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning.1998,31(1-3):29-53P
[26]Thrun S, Koller D Ghahmarani Z, et al. Simultaneous mapping and localization with sparse extended information filters:Theory and initial results. Technical report, CMU-CS-02-111, Carnegie Mellon University,2002
[27]Linaker F, Ishikawa M. Real-time appearance-based Monte Carlo localization. Robotics and Autonomous Systems.2006,54(3):205-220P
[28]武二永.基于视觉的机器人同时定位与地图构建,浙江大学博士学位论文,2007
[29]T. S. Levitt and D. T. Lawton. Qualitative navigation for mobile robots. Artificial Intelligence.1990,44(3):305-360P
[30]Thrun S. Robot Mapping:A survey. Pittsburgh:CUM-CS-02-111, Carnegie Mellon University,2002
[31]徐则中.移动机器人的同时定位和地图构建博士论文.浙江大学,2004
[32]Moravec H P, Elfes A. High resolution maps from wide angle sonar. Proc. of the 1985 IEEE International Conference onRobotics and Automation (ICRA'85).1985: 116-121P
[33]Lee S J, Lee Y C, Cho D W, et al. Evaluation of features through grid association for building a sonar map. Proc of the IEEE International Conference on Robots and Automation.2006:2615-2620P
[34]David Silver, Deryck Morales, Loannis Rekleitis, et al. Arc carving:obtaining accurate, low latency maps from ultrasonic range sensors. Proc. of IEEE International Conference on Robotics and Automation(ICRA),2004,2:1554-1561P
[35]Francesco Savelli, Benjamin Kuipers. Loop-closing and planarity in topological map-building. IEEE International Conference on Intelligent Robots and systems,2004: 1511-1517P
[36]Elfes A. Sonar based real- world mapping and navigation. IEEE Transactions on Robotics and Automation.1987,3(3):249-265P
[37]Thrun S. Learning Occupancy Grids with Forward Sensor Models. Autonomous Systems.2003,5(2):111-127P
[38]Chow K M, Rad A B, lp Y L. Enhancement of Probabilistic Grid-based Map for Mobile Robot Applications. Journal of Intelligent and Robotic Systems.2002,34: 155-174
[39]Arleo A, Millan J R, Floreano D. Efficient Learning of Variable-Resolution Cognitive Maps for Autonomous Indoor Navigation. IEEE Transactions on Robotics and Automation.1999,15(6):990-1000P
[40]Kurt Konolige. Improved Occupancy Grids for Map Building. Autonomous Robot. 1997,4:351-367P
[41]Oriolo G Vendittelli M, Ulivi G. On-line map building and navigation for autonomous mobile robots. IEEE International Conference on Robotics and Automation.1995,3: 2900-2906P
[42]Hughes K, Murphy R. Ultrasonic robot localization using Dempster-Shafer theory. Proc. of SPIE Int Conference on Society for Optical Engineering.1992:2-11P
[43]Elfes A. Multi-sources Patial data fusion using Bayesian reasoning. New York: Academic Press.1992:137-163P
[44]J. J. Leonard, H. F. Durrant-Whyte, I. J. Cox. Dynamic map building for an autonomous mobile robot. Int. Journal of Robotics Research.1992,11(4):286-298P
[45]K. Chong, L. Kleeman. Mobile robot map building from an advanced sonar array and aceurate odometry. Int. Joumal of Robotics Research.1999,18(1):20-36P
[46]Kortenkamp D and Weynouth T. Topological mapping for mobile robots using a combination of sonar and vision sensing, Proc.of the 12th National Conf. on Artificial Intelligence.1994:979-984P
[47]Fabrizi E and Saffiotti A. Augmenting topology-based maps with geometric information. Robotics and Autonomous Systems.2002,40(2-3):91-97P
[48]Jose Gaspar, Niall Winters, Jose Santos-Victor. Vision-Based Navigation and Environmental Representations with an Omnidirectional Camera. IEEE Transactions on Robotics and Automation.2000,16(6):890-898P
[49]Nicola Tomatis, Illah Nourbakhsh, Roland Siegwart. Hybrid simultaneous localization and map building:a natural integration of topological and metric. Robotics and Autonomous System.2003,44(1):3-14P
[50]Perera L D L, Wijesoma W S, Adams M D. Data Association in Dynamic Environments Using a Sliding Window of Temporal Measurement Frames. Proc. of IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS).2005: 753-758P
[51]Wijesoma W S, Perera L D L, Adams M D. Toward Multidimensional Assignment Data Association in Robot Localization and Mapping. IEEE Transactions on Robotics.2006,22(2):350-365P
[52]Davey S J. Simultaneous Localization and Map Building Using the Probabilistic Multi-Hypothesis Tracker. IEEE Transactions on Robotics.2007,23(2):271-280P
[53]Nieto J, Guivant J, Nebot E, et al. Real time data association for FastSLAM. Proc. of IEEE Int. Conference on Robotics and Automation,2003:412-418P
[54]Bailey T. Mobile Robot Localization and Mapping in Extensive Outdoor Environments. The doctoral thesis of Sydney University,2002
[55]Thrun S, Fox D, Burgard W. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning.1998,31(1-3):29-53P
[56]王璐,蔡自兴.未知环境中移动机器人并发建图与定位(CML)的研究进展.机器人.2004,26(4)：380-384P
[57]Smith R, Self M, Cheeseman P. Estimating Uncertain Spatial Relationships in Robotics:Autonomous Robot Vehicles. London:Springer-Verlag,1990
[58]Dellaert F, Fox D, Burgard W, et al. Monte Carlo localization for mobile robots. Proc. Of IEEE Int. Conf. on Robotics and Automation(ICEA'99).1999:1322-1328P
[59]Kantor G, Singh S. Preliminary Results in Range-Only Localization and Mapping. Proc. Of IEEE Int. Conf. on Robotics and Automation(ICRA'02).2002:1818-1823
[60]Guilherme N D, Avinash C K. Vision for mobile robot navigation:A survey. IEEE Transaction on Pattern Analysis and Machine Intelligence.2002,24(2):1-31P
[61]KrotKov E. Mobile robot localization using a single image. Proc. Of IEEE Int. Conference on Robotics and Automation.1989:978-983P
[62]J. Guivant, E. Nebot and S. Baiker. Localization and map building using laser range sensors in outdoor applications. Journal of Robotic Systems.2001,17(10): 565-583P
[63]D. Tardos, J. Neira and P. Newman. Robust mapping and localization in indoor environments using sonar data. International Journal of Robotics Research.2002, 21(4):311-330P
[64]迟健男,徐心和.移动机器人即时定位与地图创建问题研究.机器人.2004,26(1)：92-96页
[65]罗荣华,洪炳熔.移动机器人同时定位与地图创建研究进展.机器人.2004,26(2)：183-18页
[66]Qiu ming Zhu. Hidden Markov Model for Dynamic Obstacle Avoidance of Mobile Robot Navigation. IEEE Transanction on Robotics and Automation,1991,7(3): 390-397P
[67]王玉全.基于全景视觉的移动机器人粒子滤波定位方法研究.哈尔滨工程大学硕士论文,2009
[68]F. Linaker, M. Ishikawa. Real-time Appearance-based Monte Carlo Localization. Robotics and Autonomous Systems.2006,54(3):205-220P
[69]Lowe D G. Object recognition from local scale-invariant features. Proc. of IEEE Int. Conference on Computer Vision.1999:20-27P
[70]Bay H, Tuytelaars T, Gool L Van. SURF:Speeded up robust features. Proc. of the European Conference on Computer Vision.2006:404-417P
[71]Bay H, Tuytelaars T, Gool L Van. Speeded-up robust features(SURF). Computer Vision and Image Understanding.2008,110(3):346-359P
[72]Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision.2004,60(2):99-110P
[73]Koenderink J.The structure of image. Biological Cybernetics.1984,50(1):363-396P
[74]Lindeberg T. Scale-space for discrete signals. IEEE Transaction Pattern Analysis and Machine Intelligence.1990,12(3):187-207P
[75]Lindeberg T. Detecting Salient Blob-Like Image Structures and Their Scales with a Scale-Space Primal Sketch:A Method for Focuos-of-Attention. International Journal of Computer Vision.1993,11(3):283-318P
[76]Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision.1998,30(2):79-116P
[77]Mikolajczyk K, Schmid C. Scale&affine invariant interest point detectors. International Journal of Computer Vision.2004,60(1):63-86P
[78]T. Lindeberg. Scale- space theory:A basic tool for analysing structures at different scales. Journal of Applied Statistics.1994,21(2):224-270P
[79]Vedaldi A. An implementation of SIFT detector and descriptor. University of California,2004
[80]M. Brownand D. G. Lowe. Invariant features from interest point groups. Proc. of British Machine Vision Conference.2002:656-665P
[81]K. Mikolajczyk, C. sehmid. Comparison of affine invariant local detectors and deseriptors. Proc. of 12th European signal Processing conference.2004:45-53P
[82]K. Mikolajezy, C. Schmid. A Performance evaluation of local deseriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(10): 1615-1630P
[83]董道国,薛向阳,罗航哉.多维数据索引结构回顾.计算机科学.2002,29(3)：1-6页
[84]刘芳洁,董道国,薛向阳.度量空间中高维索引结构回顾.计算机科学.2003,30(7)：64-68页
[85]Bay H. From Wide-baseline Point and Line Correspondences to 3D. Ph.D. Thesis, ETN Zurich,2006
[86]Bay H, Fasel B, Gool L Van. Interactive museum guide:fast and robust recognition of museum objects. Proc. of the Frist International Workshop on Mobile Vision, 2006:179-191P
[87]Viola P, Jones M. Rapid object detectoin using a boosted cascade of simple features. Porc. of IEEE Conference on Computer Vision and Pattern Recghition.2001: 164-171P
[88]Simard P, Bottou L, Haffner P, et al. Boxlets:a fast convolution algorithm for signal processing and nerural networks. Advances in Neural Information Processing Systems.1999,11(2):23-30P
[89]Se S, Lowe D G, Little J J. Vision-based global localizafion and mapping for mobile robots. IEEE Transactions on Robotics.2005,21(3):364-375P
[90]Davison A J. Real-time simultaneous localisation and mapping with a single camera. Proc. of IEEE Int. Conference on Computer Vision.2003:1403-1410P
[91]Dissanayake G, Durrant_Whyte H, Bailey T. Computationally efficent Solution to the Simultaneous Localisation and Map Building(SLAM) Problem. Proc. of IEEE International Conference on Robotics and Automation.2000:1009-1014P
[92]Rudy N. Robot localization and Kalman filter. Dutch Utercht University,2003
[94]Kalman R. E. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME Journal of Basic Engineering.1960,82(1):167-179P
[95]秦永元.卡尔曼滤波与组合导航原理.西北工业大学出版社.1998
[96]Newman P M. On the Structure and solution of the simultaneous localisation and map building problem. Sydney:University of Sydney,1999
[97]Csorba M. Simultaneous localization and map building. Oxfrod:University of Oxford,1997
[98]Shojaie K, Ahmadi K, Shahri A M. Effects of Iteration in Kalman Filters Family for Improvement of Estimation Accuraey in Simultaneous Localization and Mapping. Proc. of IEEE International Conference on Advanced Intelligent Mechatronics,2007: 441-453P
[99]T. J. Tam and Y. Rasis. Observers for nonlinear stochastic systems. IEEE Transactions on Automation and Control.1976:441-448P
[100]G. Welch and G. Bishop. An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill,2001
[101]B. D. O. Anderson and J. B. Moore最佳滤波.北京：国防工业出版社,1979
[102]Durrant-Whyte H, Bailey T. Simultaneous localization and mapping(SLAM):PartⅠ the Essential Algorithms. IEEE Robotics and Automation Magazine,2006, 13(2):99-110P
[103]Tim Bailey, JuanNieto, JoseGuivant, ect. Consistency of the EKF-SLAM Algoritm. Proc. of IEEE/RSJ International Conference on Inielligent Robots and Systems.2006: 3562-3568P
[104]陈玲.移动机器人vSLAM研究与实现.上海交通大学硕士学位论文,2007
[105]Wan E A, van der Merwe R. The Unscented Kalman Filter for Nonlinear Estimation. Proc. of symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control(AS-SPCC).2000:153-158P
[106]Juliers J, Uhlmann J K. A new extension of the Kalman filter to nonlinear systems. Proc. of International Society for Optical Engineering.1997:182-193P
[107]Julier S J, Uhlmann J K, Durrant-whyte. A New Approach for Filtering Nonlinear Systems. Proc. of the Ameriean Control Conference.1995:1628-1632P
[108]Julier S J.The scaled unscented transformation. Proc. of the American Control Conference.2002:4555-4559P
[109]Wan E A, van der Merwe R.The Unscented Kalman Filter for Nonlinear Estimation. Proc. of symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control.2000:153-158P
[110]Julier S J, Uhlman J K. Unscented filtering and nonlinear estimation. Proc. of the IEEE.2004,92(3):401-422P
[111]Julier S J, Uhlmann J K. A consistent, Debiased method for converting between polar and Cartesian coordinate systems. Proc. of The International Society for Optical Engineering.1997:110-121P
[112]Juan Andrade-Cetto, Teresa Vidal-Calleja, and Alberto Sanfeliu. Unscented transformation of vehicle states in slam. Proc. of the 2005 IEEE International Conference on Robotics and Automation.2005:324-329P
[113]L. Goncalves, E. Di Bernardo, D. Benson, M. Svedman, J. Ostrowski, N. Karlsson, and P. Pirjanian. A visual front-end for simultaneous localization and mapping. Proc. Of Int. Conf. on Robotics and Automation,2005:44-49P
[114]S Huang, G Dissanayake. Convergence analysis for extended Kalman filter based SLAM. Proc. Of IEEE Int. Conf. on Robotics and Automation.2006:412-417P
[115]C. Andrieu, A. Doucet, W. Fitzgerald, and S. Godsill. An introduction to the theory and applications of simulation based computational methods in Bayesian signal Processing. Cambridge University Engineering Department,1998
[116]A. Doucet. On sequential simulation-based methods for Bayesian filtering. University Cambridge,1998
[117]Rudolph Vander Merwe, Nando De Freitas, Amaud Doucet, Eric Wan.The Unscented Particle Filter. Advance Neural Information Processing Systems. 2000:44-67P
[118]M.Pitt and N. Shephard. Filtering via simulation:Auxiliary Particle filters. Journal of the American Statistical Association.1999,94(446):590-599P
[119]Thrun S, Burgard W, Fox D. Probabilistic Robotics. New York:MIT,2003
[120]Lancaster P, Lerer L, Tismenetsky M. Factored forms for solutions of AX-B=C and X-AXB=C in companion matrices. Linear Algebra and Its Applications,1984,62(3): 19-49P
[121]Kwok C, Fox D, Meila M. Real Time particle Filters. Advances in Neural Information Processing Systems.2003,15(3):1081-1090P
[122]LIU J S. CHEN R. Monte Carlo methods for dynamic systems. American Statistical Association.1998,93(443):1032-1044P
[123]Lindquist A, Yakubovich V A. Optimal damping of forced Oscillations in discrete-time systems. IEEE. Trans Automatic Control,1997,42(6):786-802P
[124]ZARITSKII V S, SVETNIK V B, SHIMELEVICH L I. Monte-Carlo techniques in problems of optimal information processing. Automation and Remote Control,1975, 36(3):2015-2022P
[125]HAMMERSLEY J M, MORTON K W. Poor man's Monte Carlo. Journal of the Royal Statistical Society B.1954,16(1):23-38P
[126]Doucet A, Gordon N. Sequential monte Carlo methods in practice. New York: Springer-Verlag,2001
[127]Arulampalam M. S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for on-line nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing,2002,50(2):174-188P
[128]胡士强,敬忠良.粒子滤波算法综述.控制与决策.2005,20(4)：361-365P
[129]杨小军,潘泉,王睿,张洪才.粒子滤波进展与展望.控制理论与应用.2006,23(2)：261-267P
[130]N. J. Gordon, D. J. Salmond, and A. F. M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Proc. of Part F:Radar and Signal Processing,1993,140(2):107-113P
[131]J. S. Liu, R. Chen, and W. H. Wong. Rejection control and sequential importance sampling. Journal of American Statistical Association,1998,93(443):1022-1031P
[132]Carpenter J, Clifford P, Fearnhead P. An improved particle filter for non-linear problems. Radar, Sonar and Navigation,1999,146(56):2-7P
[133]Doucet A, Godsill S, Andrieu, C. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing.2000,10(3):197-208P
[134]Doucet A, J F G de Freitas. Muprhy K, and Russel S. Rao-blackwellized Particle filtering for dynamic Byaesian networks. Proc. of IEEE International Conference on Robotics and Automation.2007:3862-3869P
[135]Maybeck P. Stochastic Models, Estimation, and Control. Academic Press,1979
[136]Montemerlo M, Thrun S, Koller D and Wegbreit B. FastSLAM:A Factored Solution to the Simultaneous Localization and Mapping Problem. Proc. of the AAAI National Conference on Artificial Intelligence.2002:593-598P
[137]K. Murphy. Dynamic Bayesian Networks:Representation, Inference and Learning Phd.Thesis University of Callfornia Berkeley,2002
[138]Muprhy K P. Bayesian map learning in dynamic enviornments. Advnaces in Neural Information processing System.2000,12:1015-1021P
[139]M. Montemerio, S.T.D. Koller, B.Wegbreit. FastSLAM2.0:An Improved Particle filtering algorithm forsnnultaneous localization and mapping that provably converges. Proc. Of the Int. Conf on Artificial Intelligence,2003:1151-1156P
[140]M. Montemerlo and S. Thrun. Simultaneous localization and mapping with unknown data association using FastSLAM, Proc. Of IEEE International Conference on Robotics and Automation.2003:1985-1991P
[141]M. Arulampalam, S. Maskell, N. Gordon. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transaction on Signal Processing. 2002,50(2):174-188P
[142]王璐.未知环境中移动机器人视觉环境建模与定位研究.浙江大学博士论文,2009
[2]西格沃特,诺巴克什著.李人厚译.自主移动机器人导论.西安：西安交通大学出版社,2004
[3]史美萍.基于人机协同的月亮车路径规划技术研究.国防科学技术大学博士论文.2006
[4]陈晓东等编著.警用机器人.北京：科学出版社,2008
[5]张宏烈.移动机器人全局路径规划的研究.哈尔滨工程大学硕士论文,2002
[6]项志宇.基于激光雷达的移动机器人障碍物检测和自定位.浙江大学博士论文,2002
[7]L. Ojeda, G. Reina, J. Borenstein. Experimental Results from FLEXnav:An Expert Rule-based Dead-reckoning System for Mars Rovers. Porc. of IEEE Aerospace Conference.2004:816-825P
[8]贾一.美国的火星探测机器人.机器人技术与应用.2001,5：1-7页
[9]Gueaieb W, Miah M. S.An intelligent mobile robot navigation technique using RFID technology.IEEE Transactions on Instrumentation and Measurement.2008,57(9): 1908-1917P
[10]Morales Y, Carballo A, Takeuchi E, Aburadani A, Tsubouchi T. Autonomous robot navigation in outdoor cluttered pedestrian walkways. Journal of Field Robotics,2009, 26(8):609-635P
[11]Betke M, Gurvits L. Mobile robot localization using landmarks. IEEE Trnasaction on Robotics and Automation.1997,13(2):251-263P
[12]王卫华,陈卫华,席裕庚.基于不确定信息的移动机器人地图创建研究进展.机器人.2001,23(6)：563-56P
[13]Castellnaos J A, Neira J, Struass O, et al. Detecting high level features for mobile robot localization. Porc. of IEEE Int Conefrence on Multisensor Fusion and Integration for Intelligent Systems.1996:611-618P
[14]梁志伟,马旭东,戴先中等.基于分布式感知的移动机器人同时定位与地图创建.机器人.2009,31(1)：33-39P
[15]J. J. Leanard, H. F. Durrant-Whyte. Mobile robot localization by tracking geometric beacons. IEEE Transaction on Robotics and Automation.1991,7(3):376-382P
[16]Borenstein J, Feng L.UMBmark-a method for measuring, comparing, and correcting dead-reckoning errors in mobile robots. Technical Report UM-MEAM-94-22, University of Michigan,1994
[17]Borenstein J, Feng L. Measurement and correction of systematic odometry errors inmobile robots. IEEE Transactions on Robotics and Automation.1996,12(6): 869-880P
[18]王玉全.基于全景视觉的移动机器人同时定位与地图创建方法研究.哈尔滨工程大学博士论文,2010
[19]Stephen Se, David G. Lowe and James J. Little.Vision-based global localization and mapping for mobile robots. IEEE Transactions on Robotics.2005,21(3):364-375P
[20]许俊勇.移动机器人全景vSLAM研究.上海交通大学硕士论文,2008
[21]Chatila R, Laumond J P. and consistent world modeling for mobile robots Position referencing and and consistent world modeling for mobile robots. EEE Int Conefrence on Robotics and automation.1985:138-145P
[22]Kuipers B, Byun Y T. Robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Journal of Robotics and Autonomous Systems. 1991,8(1-2):47-63P
[23]Avots D, Lim E, Thibaux R, et al. A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments. Porc. of IEEE/RSJ International Conference Inteligent Robots and System.2002,1: 521-526P
[24]Hahnel D, Schulz D, Burgard W. Map building with mobile robots in populated environments. Advanced Robotics,2003,17(7):579-598P
[25]Thrun S, Fox D, Burgard W. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning.1998,31(1-3):29-53P
[26]Thrun S, Koller D Ghahmarani Z, et al. Simultaneous mapping and localization with sparse extended information filters:Theory and initial results. Technical report, CMU-CS-02-111, Carnegie Mellon University,2002
[27]Linaker F, Ishikawa M. Real-time appearance-based Monte Carlo localization. Robotics and Autonomous Systems.2006,54(3):205-220P
[28]武二永.基于视觉的机器人同时定位与地图构建,浙江大学博士学位论文,2007
[29]T. S. Levitt and D. T. Lawton. Qualitative navigation for mobile robots. Artificial Intelligence.1990,44(3):305-360P
[30]Thrun S. Robot Mapping:A survey. Pittsburgh:CUM-CS-02-111, Carnegie Mellon University,2002
[31]徐则中.移动机器人的同时定位和地图构建博士论文.浙江大学,2004
[32]Moravec H P, Elfes A. High resolution maps from wide angle sonar. Proc. of the 1985 IEEE International Conference onRobotics and Automation (ICRA'85).1985: 116-121P
[33]Lee S J, Lee Y C, Cho D W, et al. Evaluation of features through grid association for building a sonar map. Proc of the IEEE International Conference on Robots and Automation.2006:2615-2620P
[34]David Silver, Deryck Morales, Loannis Rekleitis, et al. Arc carving:obtaining accurate, low latency maps from ultrasonic range sensors. Proc. of IEEE International Conference on Robotics and Automation(ICRA),2004,2:1554-1561P
[35]Francesco Savelli, Benjamin Kuipers. Loop-closing and planarity in topological map-building. IEEE International Conference on Intelligent Robots and systems,2004: 1511-1517P
[36]Elfes A. Sonar based real- world mapping and navigation. IEEE Transactions on Robotics and Automation.1987,3(3):249-265P
[37]Thrun S. Learning Occupancy Grids with Forward Sensor Models. Autonomous Systems.2003,5(2):111-127P
[38]Chow K M, Rad A B, lp Y L. Enhancement of Probabilistic Grid-based Map for Mobile Robot Applications. Journal of Intelligent and Robotic Systems.2002,34: 155-174
[39]Arleo A, Millan J R, Floreano D. Efficient Learning of Variable-Resolution Cognitive Maps for Autonomous Indoor Navigation. IEEE Transactions on Robotics and Automation.1999,15(6):990-1000P
[40]Kurt Konolige. Improved Occupancy Grids for Map Building. Autonomous Robot. 1997,4:351-367P
[41]Oriolo G Vendittelli M, Ulivi G. On-line map building and navigation for autonomous mobile robots. IEEE International Conference on Robotics and Automation.1995,3: 2900-2906P
[42]Hughes K, Murphy R. Ultrasonic robot localization using Dempster-Shafer theory. Proc. of SPIE Int Conference on Society for Optical Engineering.1992:2-11P
[43]Elfes A. Multi-sources Patial data fusion using Bayesian reasoning. New York: Academic Press.1992:137-163P
[44]J. J. Leonard, H. F. Durrant-Whyte, I. J. Cox. Dynamic map building for an autonomous mobile robot. Int. Journal of Robotics Research.1992,11(4):286-298P
[45]K. Chong, L. Kleeman. Mobile robot map building from an advanced sonar array and aceurate odometry. Int. Joumal of Robotics Research.1999,18(1):20-36P
[46]Kortenkamp D and Weynouth T. Topological mapping for mobile robots using a combination of sonar and vision sensing, Proc.of the 12th National Conf. on Artificial Intelligence.1994:979-984P
[47]Fabrizi E and Saffiotti A. Augmenting topology-based maps with geometric information. Robotics and Autonomous Systems.2002,40(2-3):91-97P
[48]Jose Gaspar, Niall Winters, Jose Santos-Victor. Vision-Based Navigation and Environmental Representations with an Omnidirectional Camera. IEEE Transactions on Robotics and Automation.2000,16(6):890-898P
[49]Nicola Tomatis, Illah Nourbakhsh, Roland Siegwart. Hybrid simultaneous localization and map building:a natural integration of topological and metric. Robotics and Autonomous System.2003,44(1):3-14P
[50]Perera L D L, Wijesoma W S, Adams M D. Data Association in Dynamic Environments Using a Sliding Window of Temporal Measurement Frames. Proc. of IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS).2005: 753-758P
[51]Wijesoma W S, Perera L D L, Adams M D. Toward Multidimensional Assignment Data Association in Robot Localization and Mapping. IEEE Transactions on Robotics.2006,22(2):350-365P
[52]Davey S J. Simultaneous Localization and Map Building Using the Probabilistic Multi-Hypothesis Tracker. IEEE Transactions on Robotics.2007,23(2):271-280P
[53]Nieto J, Guivant J, Nebot E, et al. Real time data association for FastSLAM. Proc. of IEEE Int. Conference on Robotics and Automation,2003:412-418P
[54]Bailey T. Mobile Robot Localization and Mapping in Extensive Outdoor Environments. The doctoral thesis of Sydney University,2002
[55]Thrun S, Fox D, Burgard W. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning.1998,31(1-3):29-53P
[56]王璐,蔡自兴.未知环境中移动机器人并发建图与定位(CML)的研究进展.机器人.2004,26(4)：380-384P
[57]Smith R, Self M, Cheeseman P. Estimating Uncertain Spatial Relationships in Robotics:Autonomous Robot Vehicles. London:Springer-Verlag,1990
[58]Dellaert F, Fox D, Burgard W, et al. Monte Carlo localization for mobile robots. Proc. Of IEEE Int. Conf. on Robotics and Automation(ICEA'99).1999:1322-1328P
[59]Kantor G, Singh S. Preliminary Results in Range-Only Localization and Mapping. Proc. Of IEEE Int. Conf. on Robotics and Automation(ICRA'02).2002:1818-1823
[60]Guilherme N D, Avinash C K. Vision for mobile robot navigation:A survey. IEEE Transaction on Pattern Analysis and Machine Intelligence.2002,24(2):1-31P
[61]KrotKov E. Mobile robot localization using a single image. Proc. Of IEEE Int. Conference on Robotics and Automation.1989:978-983P
[62]J. Guivant, E. Nebot and S. Baiker. Localization and map building using laser range sensors in outdoor applications. Journal of Robotic Systems.2001,17(10): 565-583P
[63]D. Tardos, J. Neira and P. Newman. Robust mapping and localization in indoor environments using sonar data. International Journal of Robotics Research.2002, 21(4):311-330P
[64]迟健男,徐心和.移动机器人即时定位与地图创建问题研究.机器人.2004,26(1)：92-96页
[65]罗荣华,洪炳熔.移动机器人同时定位与地图创建研究进展.机器人.2004,26(2)：183-18页
[66]Qiu ming Zhu. Hidden Markov Model for Dynamic Obstacle Avoidance of Mobile Robot Navigation. IEEE Transanction on Robotics and Automation,1991,7(3): 390-397P
[67]王玉全.基于全景视觉的移动机器人粒子滤波定位方法研究.哈尔滨工程大学硕士论文,2009
[68]F. Linaker, M. Ishikawa. Real-time Appearance-based Monte Carlo Localization. Robotics and Autonomous Systems.2006,54(3):205-220P
[69]Lowe D G. Object recognition from local scale-invariant features. Proc. of IEEE Int. Conference on Computer Vision.1999:20-27P
[70]Bay H, Tuytelaars T, Gool L Van. SURF:Speeded up robust features. Proc. of the European Conference on Computer Vision.2006:404-417P
[71]Bay H, Tuytelaars T, Gool L Van. Speeded-up robust features(SURF). Computer Vision and Image Understanding.2008,110(3):346-359P
[72]Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision.2004,60(2):99-110P
[73]Koenderink J.The structure of image. Biological Cybernetics.1984,50(1):363-396P
[74]Lindeberg T. Scale-space for discrete signals. IEEE Transaction Pattern Analysis and Machine Intelligence.1990,12(3):187-207P
[75]Lindeberg T. Detecting Salient Blob-Like Image Structures and Their Scales with a Scale-Space Primal Sketch:A Method for Focuos-of-Attention. International Journal of Computer Vision.1993,11(3):283-318P
[76]Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision.1998,30(2):79-116P
[77]Mikolajczyk K, Schmid C. Scale&affine invariant interest point detectors. International Journal of Computer Vision.2004,60(1):63-86P
[78]T. Lindeberg. Scale- space theory:A basic tool for analysing structures at different scales. Journal of Applied Statistics.1994,21(2):224-270P
[79]Vedaldi A. An implementation of SIFT detector and descriptor. University of California,2004
[80]M. Brownand D. G. Lowe. Invariant features from interest point groups. Proc. of British Machine Vision Conference.2002:656-665P
[81]K. Mikolajczyk, C. sehmid. Comparison of affine invariant local detectors and deseriptors. Proc. of 12th European signal Processing conference.2004:45-53P
[82]K. Mikolajezy, C. Schmid. A Performance evaluation of local deseriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(10): 1615-1630P
[83]董道国,薛向阳,罗航哉.多维数据索引结构回顾.计算机科学.2002,29(3)：1-6页
[84]刘芳洁,董道国,薛向阳.度量空间中高维索引结构回顾.计算机科学.2003,30(7)：64-68页
[85]Bay H. From Wide-baseline Point and Line Correspondences to 3D. Ph.D. Thesis, ETN Zurich,2006
[86]Bay H, Fasel B, Gool L Van. Interactive museum guide:fast and robust recognition of museum objects. Proc. of the Frist International Workshop on Mobile Vision, 2006:179-191P
[87]Viola P, Jones M. Rapid object detectoin using a boosted cascade of simple features. Porc. of IEEE Conference on Computer Vision and Pattern Recghition.2001: 164-171P
[88]Simard P, Bottou L, Haffner P, et al. Boxlets:a fast convolution algorithm for signal processing and nerural networks. Advances in Neural Information Processing Systems.1999,11(2):23-30P
[89]Se S, Lowe D G, Little J J. Vision-based global localizafion and mapping for mobile robots. IEEE Transactions on Robotics.2005,21(3):364-375P
[90]Davison A J. Real-time simultaneous localisation and mapping with a single camera. Proc. of IEEE Int. Conference on Computer Vision.2003:1403-1410P
[91]Dissanayake G, Durrant_Whyte H, Bailey T. Computationally efficent Solution to the Simultaneous Localisation and Map Building(SLAM) Problem. Proc. of IEEE International Conference on Robotics and Automation.2000:1009-1014P
[92]Rudy N. Robot localization and Kalman filter. Dutch Utercht University,2003
[94]Kalman R. E. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME Journal of Basic Engineering.1960,82(1):167-179P
[95]秦永元.卡尔曼滤波与组合导航原理.西北工业大学出版社.1998
[96]Newman P M. On the Structure and solution of the simultaneous localisation and map building problem. Sydney:University of Sydney,1999
[97]Csorba M. Simultaneous localization and map building. Oxfrod:University of Oxford,1997
[98]Shojaie K, Ahmadi K, Shahri A M. Effects of Iteration in Kalman Filters Family for Improvement of Estimation Accuraey in Simultaneous Localization and Mapping. Proc. of IEEE International Conference on Advanced Intelligent Mechatronics,2007: 441-453P
[99]T. J. Tam and Y. Rasis. Observers for nonlinear stochastic systems. IEEE Transactions on Automation and Control.1976:441-448P
[100]G. Welch and G. Bishop. An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill,2001
[101]B. D. O. Anderson and J. B. Moore最佳滤波.北京：国防工业出版社,1979
[102]Durrant-Whyte H, Bailey T. Simultaneous localization and mapping(SLAM):PartⅠ the Essential Algorithms. IEEE Robotics and Automation Magazine,2006, 13(2):99-110P
[103]Tim Bailey, JuanNieto, JoseGuivant, ect. Consistency of the EKF-SLAM Algoritm. Proc. of IEEE/RSJ International Conference on Inielligent Robots and Systems.2006: 3562-3568P
[104]陈玲.移动机器人vSLAM研究与实现.上海交通大学硕士学位论文,2007
[105]Wan E A, van der Merwe R. The Unscented Kalman Filter for Nonlinear Estimation. Proc. of symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control(AS-SPCC).2000:153-158P
[106]Juliers J, Uhlmann J K. A new extension of the Kalman filter to nonlinear systems. Proc. of International Society for Optical Engineering.1997:182-193P
[107]Julier S J, Uhlmann J K, Durrant-whyte. A New Approach for Filtering Nonlinear Systems. Proc. of the Ameriean Control Conference.1995:1628-1632P
[108]Julier S J.The scaled unscented transformation. Proc. of the American Control Conference.2002:4555-4559P
[109]Wan E A, van der Merwe R.The Unscented Kalman Filter for Nonlinear Estimation. Proc. of symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control.2000:153-158P
[110]Julier S J, Uhlman J K. Unscented filtering and nonlinear estimation. Proc. of the IEEE.2004,92(3):401-422P
[111]Julier S J, Uhlmann J K. A consistent, Debiased method for converting between polar and Cartesian coordinate systems. Proc. of The International Society for Optical Engineering.1997:110-121P
[112]Juan Andrade-Cetto, Teresa Vidal-Calleja, and Alberto Sanfeliu. Unscented transformation of vehicle states in slam. Proc. of the 2005 IEEE International Conference on Robotics and Automation.2005:324-329P
[113]L. Goncalves, E. Di Bernardo, D. Benson, M. Svedman, J. Ostrowski, N. Karlsson, and P. Pirjanian. A visual front-end for simultaneous localization and mapping. Proc. Of Int. Conf. on Robotics and Automation,2005:44-49P
[114]S Huang, G Dissanayake. Convergence analysis for extended Kalman filter based SLAM. Proc. Of IEEE Int. Conf. on Robotics and Automation.2006:412-417P
[115]C. Andrieu, A. Doucet, W. Fitzgerald, and S. Godsill. An introduction to the theory and applications of simulation based computational methods in Bayesian signal Processing. Cambridge University Engineering Department,1998
[116]A. Doucet. On sequential simulation-based methods for Bayesian filtering. University Cambridge,1998
[117]Rudolph Vander Merwe, Nando De Freitas, Amaud Doucet, Eric Wan.The Unscented Particle Filter. Advance Neural Information Processing Systems. 2000:44-67P
[118]M.Pitt and N. Shephard. Filtering via simulation:Auxiliary Particle filters. Journal of the American Statistical Association.1999,94(446):590-599P
[119]Thrun S, Burgard W, Fox D. Probabilistic Robotics. New York:MIT,2003
[120]Lancaster P, Lerer L, Tismenetsky M. Factored forms for solutions of AX-B=C and X-AXB=C in companion matrices. Linear Algebra and Its Applications,1984,62(3): 19-49P
[121]Kwok C, Fox D, Meila M. Real Time particle Filters. Advances in Neural Information Processing Systems.2003,15(3):1081-1090P
[122]LIU J S. CHEN R. Monte Carlo methods for dynamic systems. American Statistical Association.1998,93(443):1032-1044P
[123]Lindquist A, Yakubovich V A. Optimal damping of forced Oscillations in discrete-time systems. IEEE. Trans Automatic Control,1997,42(6):786-802P
[124]ZARITSKII V S, SVETNIK V B, SHIMELEVICH L I. Monte-Carlo techniques in problems of optimal information processing. Automation and Remote Control,1975, 36(3):2015-2022P
[125]HAMMERSLEY J M, MORTON K W. Poor man's Monte Carlo. Journal of the Royal Statistical Society B.1954,16(1):23-38P
[126]Doucet A, Gordon N. Sequential monte Carlo methods in practice. New York: Springer-Verlag,2001
[127]Arulampalam M. S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for on-line nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing,2002,50(2):174-188P
[128]胡士强,敬忠良.粒子滤波算法综述.控制与决策.2005,20(4)：361-365P
[129]杨小军,潘泉,王睿,张洪才.粒子滤波进展与展望.控制理论与应用.2006,23(2)：261-267P
[130]N. J. Gordon, D. J. Salmond, and A. F. M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Proc. of Part F:Radar and Signal Processing,1993,140(2):107-113P
[131]J. S. Liu, R. Chen, and W. H. Wong. Rejection control and sequential importance sampling. Journal of American Statistical Association,1998,93(443):1022-1031P
[132]Carpenter J, Clifford P, Fearnhead P. An improved particle filter for non-linear problems. Radar, Sonar and Navigation,1999,146(56):2-7P
[133]Doucet A, Godsill S, Andrieu, C. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing.2000,10(3):197-208P
[134]Doucet A, J F G de Freitas. Muprhy K, and Russel S. Rao-blackwellized Particle filtering for dynamic Byaesian networks. Proc. of IEEE International Conference on Robotics and Automation.2007:3862-3869P
[135]Maybeck P. Stochastic Models, Estimation, and Control. Academic Press,1979
[136]Montemerlo M, Thrun S, Koller D and Wegbreit B. FastSLAM:A Factored Solution to the Simultaneous Localization and Mapping Problem. Proc. of the AAAI National Conference on Artificial Intelligence.2002:593-598P
[137]K. Murphy. Dynamic Bayesian Networks:Representation, Inference and Learning Phd.Thesis University of Callfornia Berkeley,2002
[138]Muprhy K P. Bayesian map learning in dynamic enviornments. Advnaces in Neural Information processing System.2000,12:1015-1021P
[139]M. Montemerio, S.T.D. Koller, B.Wegbreit. FastSLAM2.0:An Improved Particle filtering algorithm forsnnultaneous localization and mapping that provably converges. Proc. Of the Int. Conf on Artificial Intelligence,2003:1151-1156P
[140]M. Montemerlo and S. Thrun. Simultaneous localization and mapping with unknown data association using FastSLAM, Proc. Of IEEE International Conference on Robotics and Automation.2003:1985-1991P
[141]M. Arulampalam, S. Maskell, N. Gordon. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transaction on Signal Processing. 2002,50(2):174-188P
[142]王璐.未知环境中移动机器人视觉环境建模与定位研究.浙江大学博士论文,2009
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