移动机器人同时定位与地图创建方法研究
|
摘要
地面自主移动机器人是一种能够在各种地面环境中连续自主运动的车辆,其发展对国防、社会、经济和科学技术具有重大的影响力,已成为各国高科技领域的战略性研究目标。而自主导航是自主移动机器人的一个最基本需求。
同时定位与地图创建方法(SLAM)允许机器人在未知环境中,依靠自身所带的传感器递增式地创建环境地图,并同时给出机器人所在位置。自上世纪九十年代开始,该方法就吸引了国内外大量的研究者,并由于其重要的理论与应用价值被很多学者认为是实现真正全自主导航机器人的关键。近十年来,SLAM取得了令人瞩目的进展,并在室内、室外、水下、空中等多种环境下得到了实践。
本文对移动机器人同时定位与地图创建方法进行了研究,在传统方法的基础上,提出了一些改进算法及新的解决方案,以提高SLAM算法的估计精度、一致性及计算效率,扩展其使用范围。具体的研究内容包括以下几个方面:
1.从不确定性分析入手,在对SLAM中的相关性进行详细深入分析的基础上,得到了特征稀疏的两个标准,进而提出了相关优先的特征稀疏策略,从而减少大量的计算负担,计算误差却和一般传统方法相当。
2.将SUT(scaled unscented transformation)变换运用到EKF SLAM算法中,研究了SLAM的线性化问题。
3.针对Rao-Blackwellised粒子滤波SLAM(RBPF SLAM)算法的不一致现象,采用归一化估计方差(NEES)对算法的一致性进行了分析,得出粒子耗尽是造成算法不一致的原因,并分别采用辅助粒子滤波及正则粒子滤波对算法的重采样过程进行改进,提高了算法的一致性。
4.针对普通粒子滤波容易受到粒子耗尽的影响,提出了一种新的粒子滤波SLAM算法。该算法将边缘粒子滤波技术(marginal particle filter,MPF)运用到SLAM中,并利用Unscented卡尔曼滤波(UKF)来计算提议分布。新算法避免了从不断增长的高维状态空间采样,非常有效地提高了算法中的有效粒子数,大大降低了粒子的权值方差,保证了粒子的多样性,同时也满足一致性要求。
5.为了改善稀疏扩展信息滤波SLAM的性能,结合相关性分析,提出了一种改进的稀疏规则:完备信息稀疏规则。该稀疏规则考虑了预测时刻的观测信息,保留了与机器人相关性最强的主动特征。在不增加计算负担的情况下,提高了算法的精度及一致性。并对各种稀疏规则进行了深入的分析,就其优劣进行了比较,提出了一种组合的稀疏规则,以综合各自的优势,扩展使用环境范围。
6.对SLAM中的联合数据关联方法进行了研究,提出了一种快速联合相容分枝定界算法。该方法通过给定每次联合相容配对个数的上限,来达到在观测数目较大时减少计算量的目的。当观测个数大于给定上限时,将分批进行数据关联,然后把结果组合起来。采用这种方法后,关联结果与一般的联合相容分枝定界算法差别很小,但计算量却大大降低。
7.对SLAM问题中的重定位方法进行了研究,提出了一种改进的随机采样重定位方法。随机采样重定位的搜索部分的计算复杂度与观测数目成指数关系,当观测数目较大时,可通过快速联合相容分枝定界思想来达到减少计算量的目的。同时,在重定位算法中,为了防止误关联,一般会给定一个关联配对数的界限,只有配对数大于该界限时,才认为重定位是可靠的。但是,在很多情况下得到的配对数是小于给定界限的。通过引入运动约束来检验配对数较小时重定位的可靠性,以决定是否信任此时的重定位结果,达到提高重定位算法可用性的目的。
本文在最后一章对全文进行了总结,并且对今后进一步的研究方向进行了展望。
Autonomous Ground Vehicle (AGV) is an intelligent mobile robot, which can run autonomously, and continuously in real-time indoor or outdoor. The development of AGV has imposing on the defense, society, economy and academy, and becomes the tactic research object of high technology of all countries. Autonomous navigation is a fundamental problem for Autonomous Ground Vehicle.
The simultaneous localization and map building (SLAM) problem asks if it is possible for a mobile robot to be placed at an unknown location in an unknown environment and for the robot to incrementally build a consistent map of this environment while simultaneously determining its location within this map. The SLAM problem has attracted a lot of researchers with a broad rang of interests and applications since 1990s. A solution to the SLAM problem has been seen as a "holy grail" for the mobile robotics community as it would provide the means to make a robot truly autonomous. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementation of SLAM methods. SLAM has been implemented in a number of different domains from indoor robots, to outdoor, underwater and airborne systems.
This dissertation is focused on the SLAM problem. Several improved methods and novel solutions are presented in order to improve consistency and computational efficiency, and additionally extend SLAM application domains. The main content of this dissertation include the following aspects:
1. Through the analysis of uncertainty, the correlation in SLAM problem is studied. It's well-known that the correlation between features is actually the critical part of the SLAM problem. Maintaining and renewing this correlation information brings a huge computation burden. Furthermore, on the basis of having carried out deep analysis on correlation, a new feature sparse tactic named correlation priority is brought forward, which may use less features which having strong correlation to cut down large amount of the computation burden, and the computation error of this method can compare with that of some general traditional methods.
2. In SLAM problem, motion and measurement models are usually of a very high nonlinear nature. An approach is designed which aims to avoid the analytical linearization based on Taylor-series expansion of both motion and measurement models by using scaled unscented transformation.
3. The inconsistency problem of the Rao-Blackwellised particle filter (RBPF) SLAM algorithm is analyzed by using the normalized estimation error square (NEES). The result shows that it is sample impoverishment of particle filter which cause the inconsistency. So it is necessary to reduce the impact of resampling. Auxiliary particle filter and regularized particle filter are used to improve the RBPF SLAM resampling step in order to obtain consistent RBPF SLAM.
4. In order to avoid the sample impoverishment problem of particle filter, a new particle filter SLAM algorithm is proposed, which is based on the marginal particle filter and using unscented Kalman filter (UKF) to generate proposal distributions. The underlying algorithm operates directly on the marginal distribution, hence avoiding having to perform importance sampling on a space of growing dimension. Additionally, UKF can reduce linearization error and gain accurate proposal distributions. Compare with the conventional particle filter SLAM methods, the new algorithm increases the number of effective particles and reduces variance of particles weight effectively. Also, it is consistent owing to the better particle diversity. As a result, it does not suffer from some shortcomings of existing particle methods for SLAM and has distinct superiority.
5. In order to enhance the performance of sparse extended information filter (SEIF) SLAM algorithm, a novel sparsification rule is brought forward. The rule takes into account the observation information of sparsification time. The correlation can be observed globally and the features which have the strongest correlation are reserved. Therefore, the new algorithm gets higher estimation precision and more consistent results than the conventional SEIF algorithm by increasing no computation burden. Furthermore, an integrated sparsification rale is brought forward. The application environments are expanded.
6. A new data association algorithm named fast JCBB (FJCBB) is proposed by giving a bound of joint compatibility pairings based on joint compatibility branch and bound (JCBB) algorithm. FJCBB has the same association performance with JCBB. Furthermore, FJCBB's computational cost increases very slowly as the number of observations increases, this character makes FJCBB more feasible than JCBB. When the number of observations is large, FJCBB will also be adequately fast for real time implementation.
7. The relocation problem is studied and an improved random sampling (RS) relocation algorithm is proposed. In RS algorithm the search part is a component exponential in the number of observations. When the number of observations is large, its computational cost increases rapidly. Therefore, the algorithm is improved by using FJCBB algorithm. The improved algorithm's search part is linear time proportional to the number of observations. Furthermore, generally an empirical threshold of associated pairings is used to prevent false positives in relocation problem. When the number of pairings is lower than the threshold, a new method named motion constrains is brought forward to verify the relocation reliability. If it's reliable, the relocation results will be accepted.
A summary of the research conclusions and a discussion on the most promising paths of future research work are presented in the last chapter of this dissertation.
同时定位与地图创建方法(SLAM)允许机器人在未知环境中,依靠自身所带的传感器递增式地创建环境地图,并同时给出机器人所在位置。自上世纪九十年代开始,该方法就吸引了国内外大量的研究者,并由于其重要的理论与应用价值被很多学者认为是实现真正全自主导航机器人的关键。近十年来,SLAM取得了令人瞩目的进展,并在室内、室外、水下、空中等多种环境下得到了实践。
本文对移动机器人同时定位与地图创建方法进行了研究,在传统方法的基础上,提出了一些改进算法及新的解决方案,以提高SLAM算法的估计精度、一致性及计算效率,扩展其使用范围。具体的研究内容包括以下几个方面:
1.从不确定性分析入手,在对SLAM中的相关性进行详细深入分析的基础上,得到了特征稀疏的两个标准,进而提出了相关优先的特征稀疏策略,从而减少大量的计算负担,计算误差却和一般传统方法相当。
2.将SUT(scaled unscented transformation)变换运用到EKF SLAM算法中,研究了SLAM的线性化问题。
3.针对Rao-Blackwellised粒子滤波SLAM(RBPF SLAM)算法的不一致现象,采用归一化估计方差(NEES)对算法的一致性进行了分析,得出粒子耗尽是造成算法不一致的原因,并分别采用辅助粒子滤波及正则粒子滤波对算法的重采样过程进行改进,提高了算法的一致性。
4.针对普通粒子滤波容易受到粒子耗尽的影响,提出了一种新的粒子滤波SLAM算法。该算法将边缘粒子滤波技术(marginal particle filter,MPF)运用到SLAM中,并利用Unscented卡尔曼滤波(UKF)来计算提议分布。新算法避免了从不断增长的高维状态空间采样,非常有效地提高了算法中的有效粒子数,大大降低了粒子的权值方差,保证了粒子的多样性,同时也满足一致性要求。
5.为了改善稀疏扩展信息滤波SLAM的性能,结合相关性分析,提出了一种改进的稀疏规则:完备信息稀疏规则。该稀疏规则考虑了预测时刻的观测信息,保留了与机器人相关性最强的主动特征。在不增加计算负担的情况下,提高了算法的精度及一致性。并对各种稀疏规则进行了深入的分析,就其优劣进行了比较,提出了一种组合的稀疏规则,以综合各自的优势,扩展使用环境范围。
6.对SLAM中的联合数据关联方法进行了研究,提出了一种快速联合相容分枝定界算法。该方法通过给定每次联合相容配对个数的上限,来达到在观测数目较大时减少计算量的目的。当观测个数大于给定上限时,将分批进行数据关联,然后把结果组合起来。采用这种方法后,关联结果与一般的联合相容分枝定界算法差别很小,但计算量却大大降低。
7.对SLAM问题中的重定位方法进行了研究,提出了一种改进的随机采样重定位方法。随机采样重定位的搜索部分的计算复杂度与观测数目成指数关系,当观测数目较大时,可通过快速联合相容分枝定界思想来达到减少计算量的目的。同时,在重定位算法中,为了防止误关联,一般会给定一个关联配对数的界限,只有配对数大于该界限时,才认为重定位是可靠的。但是,在很多情况下得到的配对数是小于给定界限的。通过引入运动约束来检验配对数较小时重定位的可靠性,以决定是否信任此时的重定位结果,达到提高重定位算法可用性的目的。
本文在最后一章对全文进行了总结,并且对今后进一步的研究方向进行了展望。
Autonomous Ground Vehicle (AGV) is an intelligent mobile robot, which can run autonomously, and continuously in real-time indoor or outdoor. The development of AGV has imposing on the defense, society, economy and academy, and becomes the tactic research object of high technology of all countries. Autonomous navigation is a fundamental problem for Autonomous Ground Vehicle.
The simultaneous localization and map building (SLAM) problem asks if it is possible for a mobile robot to be placed at an unknown location in an unknown environment and for the robot to incrementally build a consistent map of this environment while simultaneously determining its location within this map. The SLAM problem has attracted a lot of researchers with a broad rang of interests and applications since 1990s. A solution to the SLAM problem has been seen as a "holy grail" for the mobile robotics community as it would provide the means to make a robot truly autonomous. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementation of SLAM methods. SLAM has been implemented in a number of different domains from indoor robots, to outdoor, underwater and airborne systems.
This dissertation is focused on the SLAM problem. Several improved methods and novel solutions are presented in order to improve consistency and computational efficiency, and additionally extend SLAM application domains. The main content of this dissertation include the following aspects:
1. Through the analysis of uncertainty, the correlation in SLAM problem is studied. It's well-known that the correlation between features is actually the critical part of the SLAM problem. Maintaining and renewing this correlation information brings a huge computation burden. Furthermore, on the basis of having carried out deep analysis on correlation, a new feature sparse tactic named correlation priority is brought forward, which may use less features which having strong correlation to cut down large amount of the computation burden, and the computation error of this method can compare with that of some general traditional methods.
2. In SLAM problem, motion and measurement models are usually of a very high nonlinear nature. An approach is designed which aims to avoid the analytical linearization based on Taylor-series expansion of both motion and measurement models by using scaled unscented transformation.
3. The inconsistency problem of the Rao-Blackwellised particle filter (RBPF) SLAM algorithm is analyzed by using the normalized estimation error square (NEES). The result shows that it is sample impoverishment of particle filter which cause the inconsistency. So it is necessary to reduce the impact of resampling. Auxiliary particle filter and regularized particle filter are used to improve the RBPF SLAM resampling step in order to obtain consistent RBPF SLAM.
4. In order to avoid the sample impoverishment problem of particle filter, a new particle filter SLAM algorithm is proposed, which is based on the marginal particle filter and using unscented Kalman filter (UKF) to generate proposal distributions. The underlying algorithm operates directly on the marginal distribution, hence avoiding having to perform importance sampling on a space of growing dimension. Additionally, UKF can reduce linearization error and gain accurate proposal distributions. Compare with the conventional particle filter SLAM methods, the new algorithm increases the number of effective particles and reduces variance of particles weight effectively. Also, it is consistent owing to the better particle diversity. As a result, it does not suffer from some shortcomings of existing particle methods for SLAM and has distinct superiority.
5. In order to enhance the performance of sparse extended information filter (SEIF) SLAM algorithm, a novel sparsification rule is brought forward. The rule takes into account the observation information of sparsification time. The correlation can be observed globally and the features which have the strongest correlation are reserved. Therefore, the new algorithm gets higher estimation precision and more consistent results than the conventional SEIF algorithm by increasing no computation burden. Furthermore, an integrated sparsification rale is brought forward. The application environments are expanded.
6. A new data association algorithm named fast JCBB (FJCBB) is proposed by giving a bound of joint compatibility pairings based on joint compatibility branch and bound (JCBB) algorithm. FJCBB has the same association performance with JCBB. Furthermore, FJCBB's computational cost increases very slowly as the number of observations increases, this character makes FJCBB more feasible than JCBB. When the number of observations is large, FJCBB will also be adequately fast for real time implementation.
7. The relocation problem is studied and an improved random sampling (RS) relocation algorithm is proposed. In RS algorithm the search part is a component exponential in the number of observations. When the number of observations is large, its computational cost increases rapidly. Therefore, the algorithm is improved by using FJCBB algorithm. The improved algorithm's search part is linear time proportional to the number of observations. Furthermore, generally an empirical threshold of associated pairings is used to prevent false positives in relocation problem. When the number of pairings is lower than the threshold, a new method named motion constrains is brought forward to verify the relocation reliability. If it's reliable, the relocation results will be accepted.
A summary of the research conclusions and a discussion on the most promising paths of future research work are presented in the last chapter of this dissertation.
引文
[1]H.F.Durrant-Whyte.Where am I? A tutorial on mobile vehicle localization.Industrial Robot,1994,21(2):11-16
[2]徐则中,移动机器人的同时定位和地图构建.2004,浙江大学:杭州
[3]罗荣华,洪炳铭.移动机器人同时定位与地图创建研究进展.机器人,26(2):182-186
[4]A.L.Meyrowitz,D.R.Blidberg,and R.C.Michelson.Autonomous Vehicles.Proceedings of IEEE,1996,84(8):1147-1194
[5]Nilsson N.Shakey the robot.AI Center,SRI International,1984
[6]J.M.Lowire,et al.Autonomous land vehicle(ALV) preliminary road-following demonstration.in Proc.of SPIE Intelligent Robots and Computer Vision,1985
[7]J.M.Lowire.Autonomous Land Vehicle Program Update:1987 Update.In Proc.of SPIE Mobile Robot Conference,1986,Cambridge:MA
[8]M.A.Turk and D.G.Morganthaler.VITS-A Vision System for Autonomous Land Vehicle Navigation.IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10(3):342-361
[9]M.Herbet and T.Kanade.Outdoor Scene Analysis Using Range Data.In Proc.of IEEE Conf.On Robtics and Automation,1986:1426-1432
[10]T.R.Dunlay and B.S.Steven.Parallel Off-road Perception Processing on the Autonomous Land Vehicle.in Proc.of SPIE Mobile Robots Ⅲ,1988:40-48
[11]R.T.Dunlay and D.G.Morgenthaler.Obstacle Avoidance On Roadways using Range Data.In SPIE Conf.On Mobile Robots,1986:110-116
[12]R.T.Dunlay.Obstacle Avoidance Perception Processing for the Autonomous Land Vehicle.In Proc.of IEEE Int.Conf.On Robotics and Automation,1988:912-917
[13]U.K.Sharma and L.S.Davis.Road Following by an Autonomous Vehicle Using Range Data.IEEE Trans.On Robotics and Automation,1988,RA-4(5):5151-523.
[14]J.J.Nitao.Computer Modeling:A Structured Light Vision System for a Mars Rover.In SPIE Vol.1195 Mobile Robots Ⅵ,1989:168-176
[15]D.H.Croquist.Development of a Martian Surface Model for Simulation of Vehicle Dynamic and Mobility.In SPIE On Mobile Robots Ⅳ,1989:157-167
[16]R.L.Karin and O.kcith.Intersection Navigation for Unmanned Ground Vehicles.In SPIE Vol.2738,1996:14-25
[17]T.Balch and R.C.Arkin.Communication in Reactive Multiagent Robotics System.Autonomous Robots,1995,1(1):27-52
[18]C.M.Shoemaker and J.A.Bomstein.Overview of the Demo Ⅲ UGV Program.in Part of the SPIE Conf.on Robotic and Semi-Robotic Ground Vehicle Technology,1998:202-211
[19]殷波,机器人的同时定位和地图创建方法研究.2006,中国海洋大学:青岛.
[20]S.Thrun M.Montemerlo,H.Dahlkamp,et al.Stanley:The Robot That Won The DARPA Grand Challenge.Journal of Field Robotics,2006,23(9):661-692
[21]C.Urmson,C.Ragusa,D.Ray.et al.A Robust Approach to High-Speed Navigation for Unrehearsed Desert Terrain.Journal of Field Robotics,2006,23(8):467-508
[22]L.Matthies,Y.Xiong,R.Hogg,et al.A Portable,Autonomous,Urban Reconnaissance Robot.Robotics and Autonomous Systems,2002,40(2-3):163-172
[23]S.B.Goldberg,M.W.Maimone,and L.Matthies.Stereo Vision and Rover Navigation Software for Planetary Exploration.In Proceedings of the 2002 IEEE Aerospace Conference.2002,Big Sky,Montana,USA
[24]Elrob.2006[cited;Available from:http://www.elrob2006.org/
[25]刘燕军.国际军用机器人合作不断扩大.军用机器人快报,1991(10)
[26]P.Boulanger,F.Blais.Range Image Segmentation,Free Space Determination,and Position Estimate for a Mobile Vehicle.in Proc.of SPIE On Mobile Robots Ⅶ,1992:444-455
[27]T.Yiengwattanon,e.a.A Method of Autonomous Vehicle Guidance By using White Dash-line Recognition.In Proc.of 2nd Asian Conference on Computer Vision,1995
[28]王宏.运动机器人体系结构与系统设计.机器人,1993,15:49-54
[29]张朋飞,何克忠,欧阳正拄等.多功能室外智能移动机器人实验平台-THMR-V.机器人,2002,24(2):97-101.
[30]孙振平,安向京,贺汉根.CITAVT-Ⅳ—视觉导航的自主车.机器人,2002,24(2):115-121
[31]王荣本,储江伟等.一种视觉导航的实用型AGV设计.机械工程学报,2002,38(11):135-138
[32]Q.Li,N.Zheng,and H.Cheng.Springrobot:a prototype autonomous vehicle and its algorithms for lane detection.IEEE Trans.on Intelligent Transportation Systems,2004,5(4):300-308
[33]周文晖,智能机器人视觉系统研究.2005,浙江大学:杭州
[34]Roumeliontis S I,Bekey G A.Bayesian estimation and Kalman filtering a unified framework for mobile robot localization.Proceedings of the IEEE International Conference on Robotics and Automation.USA,2000:2985-2992
[35]Fox D,Burgard W,Thnm S.Markov localization for mobile robots in dynamic environments.Journal of Artificial Intelligence Research,1999,11(3):391-427
[36]Cox I,Wilfong G.Autonomous robot vehicle.London Springer-Verlag,1990:167-193
[37]Burgard W.,Fox D.,Henning D.,Schmidt T.Estimating the absolute position of a mobile robot using position probability grids.In Proceedings of the Thirteenth National Conference on Artificial Intelligence(AAAI'99),1996:896-901
[38]Burgard W.,Fox D.,Henning D.,Schmidt T.Position tracking with position probability grids.In Proceeding of the 1st Euromicro Workshop on Advanced Mobile Robots.IEEE Computer Society Press,1996
[39]Xiang Zhiyu,Liu Jilin.Initial Localization for Indoor Mobile Robots Using a 2D Laser Range Finder.In Proceeding of SPIE Vol.4573 mobile robot ⅩⅥ,Douglas W.Gage,2002:168-177
[40]Xu Zezhong,Liang Ronghua,Liu Jilin.Global localization based on comer point.In IEEE International Symposium on Computational Intelligence in Robotics and Automation,2002:843-847
[41]I.J.Cox,Blanche.An experiment in guidance and navigation of an autonomous robot vehicle.IEEE Transaction on Robotics and Automation,1991,7(2):193-204
[42]F.Lu,E.Milions.Robot pose estimation in unknown environments by matching 2D rang scans.In IEEE Int.Conf.on Computer Vision and Pattern Recongnition,1994:935-938
[43]J.S.Gutmann,C.Schlegel,AMOS.Comparison of Scan Matching Approaches for Self-localization in Indoor Environment.In Proceeding of the 1st Euromicro Workshop in Advanced Mobile Robots,1996:61-67
[44]J.Weber,K.W.Jorg,E.Von Puttkamer.APR-Global Scan Matching Using Anchor Point Relationships.In Proceeding of the 6th Int.Conf.on Intelligence Autonomous System,2000:471-478
[45]厉茂海,洪炳熔.移动机器人的概率定位方法研究进展.机器人,2005,27(4):380-384
[46]Jensfelt P.Approaches to mobile robot localization in indoor environments.Sweden Royal Institute of Technology,2001
[47]Burgard W.,Derr A,Fox D.Integrating global position estimation and position tracking for mobile robots:the dynamic Markov localization approach.In Proc.IEEE/RSJ Int.Conf.on Intelligent Robots and System,1998:730-735
[48]Qingxiang Wu,Bell DA,et al.Rough computational methods on reducing cost of computation in Markov localization for mobile robots.In Proc.the 4th World Congress on Intelligence Control and Automation,2002:1226-1230
[49]Gustafsson F.,Gunnarsson F.,Bergman N.,et al.Particle filters for positioning,navigation,and tracking.IEEE Transactions on Signal Processing,2002,50:425-435
[50]郭剑辉,赵春霞,石杏喜.基于联邦UKF算法的移动机器人自主组合导航.计算机工程与应用,2007,43(32):59-61
[51]Rudy N.Robot localization and Kalman filters.Dutch Utrecht University,2003
[52]Gutmann J.S.,Weigel T.,Nebel B.A fast,accurate,and robust method for self-localization in polygonal environments using laser range finders.Advanced Robotics Journal,2002,14(8):651-668
[53]Locchi L,Mastrantuono D,Nardi D.A probabilistic approach to Hough localization.In International Conference on Robotics and Automation,Seoul,Korea,2001
[54]Leonard J.,Durrant-Whyte H.Mobile robot localization by tracking geometric beacons.IEEE Transactions on Robotics and Automation,1991,7(3):376-382
[55]Jensfelt P.,Christensen H.I.Laser based pose tracking.In proceedings of IEEE International Conference on Robotics and Automation,1999:2994-3000
[56]Wada M.,Yoon KS,Hashimoto H.High accuracy road vehicle state estimation using extended Kalman filter.In Proceedings of 3rd IEEE International Conference on Intelligent Transportation Systems,2000:282-287
[57]Fox D.Markov localization:A Probabilistic framework for mobile robot localization and navigation.German:University of Bonn,1998
[58]Thrun S.,Fox D.,Burgard W,et al.Robust Monte Carlo localization for mobile robot.Artificial Intelligence,2001,128(1-2):99-141
[59]Julier S.J,Uhlman J.k,Durrant-Whyte H.A new approach for the nonlinear transformation of means and covariance in linear filters.IEEE Transaction on Automatic Control,2000,5:477-482
[60]Ito K.,Xiong K.Gaussian for nonlinear filtering problems.IEEE Transaction on Automatic Control,2000,45(5):910-927
[61]Alspach D,Sorenson.Nonlinear Bayesian estimation using Gaussian sum approximations.IEEE Transaction on Automatic Control,1972,AC(17):439-448
[62]Sorenson,Alspach D.Recursive Bayesian estimation using Gaussian sum Automatic,1971,7(2):465-479
[63]T.Bailey.Mobile Robot Localisation and Mapping in Extensive Outdoor Environments.PHD thesis,Australian Centre for Field Robotics,Department of Aerospace,Mechanical and Mechatronic Engineering,University of Sydney,August 2002
[64]T.Bailey,E.Nebot,J.Rosenblatt,and H.Durrant-Whyte.Robust Distinctive Place Recognition for Topological Maps.In Proceedings of International Conference on Field and Service Robotics.August 1999,Pittsburgh,PA,USA
[65]B.Kuipers,Y.Byun.A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations.IEEE Journal of Robotics and Autonomous System, 1991,8:47-63
[66]A.Elfes.Occupancy Grids: A Stochastic Spatial Representation for Active Robot Perception.In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence.July 1990
[67]M.Martin, H.Moravec.Robot Evidence Grids.Technical Report: CMURI-TR-96-06, Carnegie Mellon University Robotics Institute, Pittsburgh, PA 15213,March 1997
[68]M.Ribo, A.Pinz.A Comparison of Three Uncertainty Calculi for Building Sonar-based Occupancy Grids.IEEE Journal of Robotics and Autonomous Systems,2001,35 :201-209
[69]S.Thrun.Learning Occupancy Grids with Forward Models.In Proceedings of the IEEE/RSJ International Conference on Intelligent Robotics and Systems, Maui, Hawaii, USA,2001:1676-1681
[70]H.Moravec, A.Elfes.High-resolution Maps from Wide-angle Sonar.In Proceedings of the IEEE International Conference on Robotics and Automation.M arch 1985:116-121
[71]A.Amir, A.Efrat, P.Indyk, H.Samet.Efficient Regular Data Structures and Algorithm for Location and Proximity Problems.In IEEE Symposium on Foundations of Computer Science, 1999:160-170
[72]K.Chong, L.Kleeman.Mobile robot map building from an advanced sonar array and accurate odometry.Intl.Journal of Robotics Research, 1999,1 8(1):20-36
[73]J.J.Leonard,H.F.Durrant-Whyte, I J.Cox.Dynamic map building for an autonomous mobile robot.Intl.Journal of Robotics Research, 1992,ll(4):286-298
[74]0.Aycard, F.Charpillet, D.Fohr, J.Mari.Place Learning and Recognition Using Hidden Markov Models.In Proceedings of the IEEE International Conference on Intelligent Robots and Systems,1997: 1741-1746
[75]R.Simmons, S.Koenig.Probabilistic Robot Navigation in Partially Observable Environments.International Joint Conference on Artificial Intelligence, 1995: 1080-1087
[76]S.Thrun.Learning Metric-Topological Maps for Indoor Mobile Robot Navigation.Artificial Intelligence, 1998, 99(1):21-27
[77]B.Yamauchi, P.Langley.Place recognition in dynamic environments.Journal of Robotic Systems,1997, 14(2):107-120
[78]S.Simhon, G.Dudek.A global topological map formed by local metric maps.In IEEE/RSJ International Conference on Intelligent Robots and Systems, 1998, 1708-1714
[79]K.S.Chong and L.Kleeman.Feature-based mapping in real, large scale environments using an ultrasonic array.International Journal Robotics Research, 1999, 18(1):3-19
[80]H.Choset and K.Nagatani.Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization.IEEE Transactions on Robotics and Automation,2001, 17(2):125-137
[81]J.A.Castellanos, J.M.M.Montiel, J.Neira, and J.D.Tard'os.The SPmap: A probabilistic framework for simultaneous localization and map building.IEEE Trans, on Robotics and Automation,1999,15(5):948-952
[82]J.Guivant and E.Nebot.Compressed filter for real time implementation of simultaneous localization and map building.In Aarne Halme, Raja Chatila, and Erwin Prassler, editors, Int.Conf.on Field and Service Robots, 2001:309-314
[83]J.J.Leonard, R.Carpenter, and H.J.S.Feder.Stochatic mapping using forward look sonar.Robotica,2001,19:467-480
[84]J.H.Kim and S.Sukkarieh.Airborne simultaneous localisation and map buildingfC].In IEEE Int.Conf.on Robotics and Automation, Taipei,Taiwan, September 2003
[85]R.Smith and P.Cheesman.On the representation of spatial uncertainty.Int.J.Robot.Research, 1987,5(4):56-68
[86]H.F.Durrant-Whyte.Uncertain geometry in robotics.IEEE Trans.Robot.Automation,1988,4(1):23-31
[87]N.Ayache and 0.Faugeras.Building, registrating, and fusing noisy visual maps.Int.J.Robot.Research,1988,7(6): 45-65
[88]J.Crowley.World modeling and position estimation for a mobile robot using ultra-sonic ranging.In Proc.IEEE Int.Conf.Robotics and Automation, 1989 :674-681
[89]R.Chatila and J.P.Laumond.Position referencing and consistent world modeling for mobile robots.In Proc.IEEE Int.Conf.Robotics and Automation, 1985:138-143
[90]R.Smith, M.Self, and P.Cheeseman.Estimating uncertain spatial relationships in robotics.In I.J.Cox and G.T.Wilfon, editors, Autonomous Robot Vehicles, pages 167-193.Springer-Verlag, 1990
[91]J.J.Leonard and H.F.Durrant-Whyte.Directed Sonar Navigation.Kluwer Academic Press, 1992
[92]W.D.Renken.Concurrent localization and map building for mobile robots using ultrasonic sensors.In Proc.IEEE Int.Workshop on Intelligent Robots and Systems (IROS), 1993
[93]H.Durrant-Whyte, D.Rye, and E.Nebot.Localization of automatic guided vehicles.In G.Giralt and G Hirzinger, editors, Robotics Research: The 7th International Symposium (ISRR'95),pages 613-625.Springer Verlag, 1996
[94]M.Csorba.Simultaneous Localization and Map Building.PhD thesis, University of Oxford, 1997
[95]M.Csorba and H.F.Durrant-Whyte.A new approach to simultaneous localization and map building.In Proceedings of SPIE Aerosense, Orlando, 1996
[96]J.J.Leonard and H.J.S.Feder.A computational efficient method for large-scale concurrent mapping and localisation.In J.Hollerbach and D.Koditscheck, editors, Robotics Research, The Ninth International Symposium (ISRR'99), pages 169-176.Springer Verlag, 2000
[97]J.A.Castellanos, J.M.Martnez, J.Neira, and J.D.Tardos.Experiments in multisensor mobile robot localization and map building.In Third IFAC Symposium on Intelligent Autonomous Vehicles,1998:173-178
[98]J.A.Castellanos, J.D.Tardos, and G.Schmidt.Building a global map of the environment of a mobile robot: The importance of correlations.In IEEE International Conference on Robotics and Automation,1997:1053-1059
[99]J.Guivant, E.M.Nebot, and S.Baiker.Localization and map building using laser range sensors in outdoor applications.Journal of Robotic Systems, 2000,17(10):565-583
[100]S.B.Williams, P.Newman, GDissanayake, and H.F.Durrant-Whyte.Autonomous underwater simultaneous localisation and map building.In Proc.IEEE International Conference on Robotics and Automation (ICRA), pages 1793-1798, San Francisco, USA, April 2000
[101]T.Bailey, J.Nieto, J.Guivant, M.Stevens, and E.Nebot.Consistency of the EKF-SLAM algorithm.IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006: 3562-3568
[102]M.Deans and M.Hebert.Experimental comparison of techniques for localization and mapping using a bearing-only sensor.In Int.Symp.Experimental Robotics, 2000
[103]J.Hollerbach and D.Koditscheck (Editors).Robotics Research, The Ninth International Symposium (ISRR'99).Springer Verlag, 2000
[104]S.Thrun, D.Fox, and W.Burgard.A probabilistic approach to concurrent mapping and localization for mobile robots.Machine Learning, 1998, 31(1):29-53
[105]Durrant-Whyte, H., Bailey, T.Simultaneous localization and mapping: part I.IEEE Robotics and Automation Magazine, September 2006, 13(2):99-110
[106]Bailey, T., Durrant-Whyte, H.Simultaneous localization and mapping: part EL IEEE Robotics and Automation Magazine, September 2006,13(3): 108-117
[107]J.Guivant and E.Nebot.Optimization of the simultaneous localization and map building algorithm for real time implementation.IEEE Transactions on Robotics and Automation, 2001, 17(3):242-257
[108]J.Knight, A.Davison, and I.Reid.Towards constant time SLAM using postponement.In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001:405-413
[109]S.B.Williams.Efficient Solutions to Autonomous Mapping and Navigation Problems.PhD thesis, University of Sydney, Australian Centre for Field Robotics, 2001.
[110]J.D.Tard'os, J.Neira, P.M.Newman, and J.J.Leonard.Robust mapping and localization in indoor environments using sonar data.International Journal of Robotics Research, 2002,21(4):311-330
[111]S.Thrun, D.Koller, Z.Ghahmarani, and H.Durrant-Whyte.SLAM updates require constant time.School of Computer Science, Carnegie Mellon University, Pittsburgh, Technical Report, 2002
[112]S.Thrun, Y.Liu, D.Koller, A.Ng, Z.Ghahramani, and H.Durrant-Whyte.Simultaneous localization and mapping with sparse extended information filters.International Journal of Robotics Research,July-August 2004, 23(7-8):693-716
[113]M.Paskin.Thin junction tree filters for simultaneous localization and mapping.Proceedings of the 18th International Joint Conference on Artificial Intelligence, San Francisco, 2003:1157-1164
[114]U.Frese.An 0(log n) algorithm for simultaneous localization and mapping of mobile robots in indoor environments.Ph.D dissertation, University of Erlangen-Nurnberg, 2004
[115]Y.Liu and S.Thrun.Results for Outdoor-SLAM Using Sparse Extended Information Filters.IEEE International Conference on Robotics and Automation, vol.1, September 2003: 1227-1233
[116]U.Frese.A proof for the approximate sparsity of SLAM information matrices .Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, April 2005
[117]R.Eustice, M.Walter, and J.Leonard.Sparse extended information filters: Insights into sparsification.Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton,Alberta, Canada, August 2005
[118]M.Walter, R.Eustice, and J.Leonard.A provably consistent method for imposing sparsity in feature-based SLAM information filters.Intl.Symp.on Robotics Research, October 2005
[119]M.R.Walter, R.M.Eustice, and J.J.Leonard.Exactly sparse extended information filters for feature-based SLAM, Intl.J.Robotics Research, April 2007,26 (4): 335-359
[120]F.Dellaert, Michacel Kaess.Square root SAM: Simultaneous location and mapping via square root information smoothing.International Journal of Robotics Research, Dec.2006,25(12):1181-1203
[121]R.M.Eustice, H.Singh, J.J.Leonard.Exactly Sparse Delayed-State Filters for View-Based SLAM.IEEE Transaction on Robotics, Dec.2006,22(6):1100-1114
[122]J.Folkesson and H.I.Christensen.Graphical SLAM - a self correcting map.In IEEE International Conference on Robotics and Automation, 2004 : 791-798
[123]Z.Wang, S.Huang, and G.Dissanayake.Implementation issues and experimental evaluation of D-SLAM.In International Conference on Field and Service Robotics, 2005
[124]R.Eustice, H.Singh, J.Leonard, M.Walter, and R.Ballard.Visually navigating the RMS Titanic with SLAM information filters.In Robotics: Science and Systems, 2005
[125]J.Guivant and E.Nebot.Improving computational and memory requirements of simultaneous localization and map building algorithms.In IEEE International Conference on Robotics and Automation, 2002: 2731-2736
[126]J.Leonard and P.Newman.Consistent, convergent, and constant-time SLAM.In International Joint Conference on Artificial Intelligence, 2003
[127]S.J.Julier and J.K.Uhlmann.A non-divergent estimation algorithm in the presence of unknown correlations.In American Control Conference, volume 4,1997: 2369-2373
[128]M.Bosse, P.Newman, J.Leonard, M.Soika, W.Feiten, and S.Teller.An Atlas framework for scalable mapping.In IEEE International Conference on Robotics and Automation, 2003, 1899-1906
[129]M.Bosse, P.Newman, J.Leonard, and S.Teller.Simultaneous localization and map building in large-scale cyclic environments using the Atlas framework.International Journal of Robotics Research,2004,23(12):1113-1140
[130]C.Estrada, J.Neira, and J.D.Tardos.Hierarchical SLAM: Realtime accurate mapping of large environments.IEEE Transactions on Robotics, 2005,21(4):588-596
[[131]Sen Zhang, Lihua Xie, Adams M.An efficient data association approach to simultaneous localization and map building.IEEE International Conference on Robotics and Automation, 2004, Volume 1,Page(s):854 -859
[132]W.Sardha Wijesoma, L.D.L.Perera, M.D.Adams.Toward Multidimensional Assignment Data Association in Robot Localization and Mapping.IEEE Transactions on Robotics, April 2006,22(2):350-365
[133]J.Neira and J.D.Tardos.Data association in stochastic mapping using the joint compatibility test.IEEE Trans.Robot.Autom, Dec.2001,17(6): 890-897
[134]Y.Bar-Shalom and T.E.Fortmann.Tracking and Data Association.Boston, MA: Academic, 1988.
[135]I.J.Cox and J.J.Leonard.Modeling a dynamic environment using a bayesian multiple hypothesis approach.Artificial Intelligence, 1994,66(2): 311-344
[136]J.Nieto, J.Guivant, E.Nebot, and S.Thran.Real time data association for FastSLAM.IEEE Int.Conf.Robot.Autom., Taipei,Taiwan, R.O.C., Sep.14-19,2003:412-418
[137]M.Montemerlo and S.Thran.Simultaneous localization and mapping with unknown data association using FastSLAM.In IEEE International Conference on Robotics and Automation, 2003: 1985-1991
[138]J.Neira, J.D.Tard'os, and J.A.Castellanos.Linear time vehicle relocation in SLAM.IEEE Int.Conf.on Robotics and Automation, Taipei, Taiwan, September 2003:427-433
[139]J.J.Leonard and R.J.Rikoski.Incorporation of delayed decision making into stochastic mapping.In International Symposium On Experimental Robotics, 2000
[140]J.J.Leonard, R.J.Rikoski, P.M.Newman, and M.C.Bosse.Mapping partially observable features from multiple uncertain vantage points.International Journal of Robotics Research, 2002, 21(10-ll):943-975
[141]T.Bailey.Constrained initialisation for bearing-only SLAM.In IEEE International Conference on Robotics and Automation, 2003
[142]J.Sofa, A.Monin, M.Devy, and T.Lemaire.Undelayed initialization in bearing only SLAM.In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005: 2751-2756
[143]J.Nieto, T.Bailey, and E.Nebot.Scan-SLAM: Combining EKFSLAM and scan correlation.In International Conference on Field and Service Robotics, 2005
[144]A.J.Davison, Y.G.Cid, and N.Kita.Real-time 3D SLAM with wide-angle vision.In IFAC/EURON Symposium on Intelligent Autonomous Vehicles, 2004
[145]P.Newman, D.Cole, and K.Ho.Outdoor SLAM using visual appearance and laser ranging.In IEEE International Conference on Robotics and Automation, 2006:1180-1187
[146]K.Konolige.Large-scale map-making.In National Conference on AI (AAAI), 2004: 457-463
[147]D.Hahnel, W.Burgard, D.Fox, and S.Thran.An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements.In IEEE/RSJ International Conference on Intelligent Robots and Systems,2003:206-211
[148]A.I.Eliazar and R.Parr.DP-SLAM 2.0.In IEEE International Conference on Robotics and Automation,2004:1314-1320
[149]G.Grisetti,C.Stachrtiss,and W.Burgard.Improving gridbased SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling.In IEEE International Conference on Robotics and Automation,2005:667-672
[150]D.Hahnel,R.Triebel,W.Burgard,and S.Thrun.Map building with mobile robots in dynamic environments.In IEEE International Conference on Robotics and Automation,2003:1557-1563
[151]C.C.Wang,C.Thorpe,and S.Thrun.On-line simultaneous localisation and mapping with detection and tracking of moving objects.In IEEE International Conference on Robotics and Automation,2003:2918-2924
[152]罗荣华,洪炳铭.基于信息融合的同时定位与地图创建研究.哈尔滨工业大学学报,2004,36(5):56-569
[153]G Dissanayake,P Newman,H F Durrant-Whyte,S Clark,M Csobra.A solution to the simultaneous localisation and map-maping(SLAM) problem.IEEE Trans.Robotics and Automation,2001,17(3):229-241
[154]H F Durrant-Whyte,G Dissanayake,P W Gibbens.Toward deployment of large-scale simultaneous localization and map building(SLAM) system.The Ninth Int.Symposium on Robotics Research.Lorne,Australia:ISRR press,2001:229-241
[155]S.Julier,J.Uhlmann,and H.F.Durrant-Whyte.A new method for the nonlinear transformation of means and covariances in filters and estimators.IEEE Trans.Automat.Contr.,2000,45(3):477-482
[156]S.J.Julier,and J.K.Uhlmann.The Scaled Unscented Transformation.In IEEE American Control Conference,Anchorage AK,USA,8-10 May 2002:4555-4559
[157]S.J.Julier and J.K.Uhlmann.Unscented filtering and nonlinear estimation.Proceedings of the IEEE,2004,92(3):401-422
[158]Juan Andrade-Cetto,Teresa Vidal-Calleja,and Alberto Sanfeliu.Unscented transformation of vehicle states in slam.In Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 05),Barcelona,Spain,18-20 April 2005:324-329
[159]Y.Bar-Shalom,X.Rong Li,and T.Kirubarajan.Estimation with Applications to Tracking and Navigation[M].Wiley InterScience,2001
[160]S.J.Julier and J.K.Uhlmann.A counter example to the theory of simultaneous localization and map building.IEEE International Conference on Robotics and Automation,2001:4238-4243
[161]J.A.Castellanos,J.Neira,and J.D.Tardos.Limits to the consistency of EKF-based SLAM.IFAC Symposium on Intelligent Autonomous Vehicles,2004
[162]S.Huang and G.Dissanayake.Convergence analysis for extended Kalman filter based SLAM.IEEE International Conference on Robotics and Automation,2006
[163]K.Murphy.Bayesian map learning in dynamic environments.Proc.of the Conf.on Neural Information Processing Systems,NIPS,Denver,CO,USA,1999:1015-1021
[164]A.Doucet,J.de Freitas,K.Murphy,S.Russel.RaoBlackwellizedparticle filtering for dynamic Bayesian networks.Proc.of the Conf.on Uncertainty in Artificial Intelligence,UAI,Stanford,CA,USA,2000:176-183
[165]M.Montemerlo,S.T.D.Koller,B.Wegbreit.FastSLAM 2.0:An improved particle filtering algorithm for simultaneous localization and mapping that provably converges.Proc.of the Int.Conf.on Artificial Intelligence,IJCAI,Acapulco,Mexico,2003:1151-1156
[166]M.Montemerlo,S.Thrun,D.Koller,B.Wegbreit.FastSLAM:A factored solution to simultaneous localization and mapping.Proc.of the National Conference on Artificial Intelligence,AAAI,Edmonton,Canada,2002:593-598
[167]厉茂海,洪炳熔,罗荣华.用改进的Rao-Blackwellized粒子滤波器实现移动机器人同时定位和地图创建.吉林大学学报(工学版),2007,37(2):401-406
[168]余洪山,王耀南.基于粒子滤波器的移动机器人定位和地图创建研究进展.机器人,2007,29(3):281-289
[169]黄庆成,洪炳熔,厉茂海,罗荣华.基于主动环形闭合约束的移动机器人分层同时定位和地图创建.计算机研究与发展,2007,44(4):636-642
[170]N.Gordon,D.Salmond,A.F.M.Smith.Novel Approach to Non-linear and Non-Gaussian Bayesian State Estimation.Proc of Institute Electric Engineering,1993,140(2):107-113
[171]Sanjeev Arulampalam,Simon Maskell,Neil Gordon,Tim Clapp.A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking.IEEE Trans Signal Proceeding,2002:174-188
[172]M.Pitt,N.Shephard.Filtering via simulation:Auxiliary particle filters.Journal of the American Statistical Association,1999,94(446):590-599
[173]C.Musso,N.Oudjane,F.LeGland.Improving Regnlafised particle filters.Sequential Monte Carlo Methods in practicle,eds A.Doucet,J.F.G.de Freitas and N.J.Gordon.2001,Springer-verlag,New York
[174]M.Klaas,N.de Freitas,and A.Doucet.Toward practical n2 Monte Carlo:The marginal particle filter.Proceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence(UAI-05).Arlington,Virginia:AUAI Press,2005,308-315
[175]D Lang,MKlaas,and N de Freitas.Empirical testing of fast kernel density estimation algorithms.Department of Computer Science,UBC,Technical Report:TR-2005-03,2005
[176]Ryan Eustice,Hanumant Singh,and John Leonard.Exactly Sparse Delayed-State Filters.Proceedings of the 2005 IEEE International Conference on Robotics and Automation,April 2005:2428-2435
[177]J.H.Lim and J.J.Leonard.Mobile robot relocation from echolocation constraints.IEEE Trans.Pattern Analysis and Machine Intelligence,September 2000,22(9):1035-1041
[178]W.E.L.Grimson.Object Recognition by Computer:The Role of Geometric Constraints.The MIT Press,Cambridge,Mass.,1990
[179]T.Bailey,E.M.Nebot,J.K.Rosenblatt,and H.F.Durrant-Whyte.Data association for mobile robot navigation:A graph theoretic approach.IEEE Int.Conf.Robotics and Automation,San Francisco,California,2000:2512-2517
[180]D.Fox,W.Burgard,D.F.,and S.Thrun.Monte-Carlo localization:Efficient position estimation for mobile robots.Proc.of the Sixteenth National Conference on Artificial Intelligence(AAAI-99),Orlando,Florida,1999:343-349
[181]D.Fox,W.Burgard,and S.Thrun.Active markov localization for mobile robots.Robotics and Autonomous Systems,1998,25(3-4):195-207
[2]徐则中,移动机器人的同时定位和地图构建.2004,浙江大学:杭州
[3]罗荣华,洪炳铭.移动机器人同时定位与地图创建研究进展.机器人,26(2):182-186
[4]A.L.Meyrowitz,D.R.Blidberg,and R.C.Michelson.Autonomous Vehicles.Proceedings of IEEE,1996,84(8):1147-1194
[5]Nilsson N.Shakey the robot.AI Center,SRI International,1984
[6]J.M.Lowire,et al.Autonomous land vehicle(ALV) preliminary road-following demonstration.in Proc.of SPIE Intelligent Robots and Computer Vision,1985
[7]J.M.Lowire.Autonomous Land Vehicle Program Update:1987 Update.In Proc.of SPIE Mobile Robot Conference,1986,Cambridge:MA
[8]M.A.Turk and D.G.Morganthaler.VITS-A Vision System for Autonomous Land Vehicle Navigation.IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10(3):342-361
[9]M.Herbet and T.Kanade.Outdoor Scene Analysis Using Range Data.In Proc.of IEEE Conf.On Robtics and Automation,1986:1426-1432
[10]T.R.Dunlay and B.S.Steven.Parallel Off-road Perception Processing on the Autonomous Land Vehicle.in Proc.of SPIE Mobile Robots Ⅲ,1988:40-48
[11]R.T.Dunlay and D.G.Morgenthaler.Obstacle Avoidance On Roadways using Range Data.In SPIE Conf.On Mobile Robots,1986:110-116
[12]R.T.Dunlay.Obstacle Avoidance Perception Processing for the Autonomous Land Vehicle.In Proc.of IEEE Int.Conf.On Robotics and Automation,1988:912-917
[13]U.K.Sharma and L.S.Davis.Road Following by an Autonomous Vehicle Using Range Data.IEEE Trans.On Robotics and Automation,1988,RA-4(5):5151-523.
[14]J.J.Nitao.Computer Modeling:A Structured Light Vision System for a Mars Rover.In SPIE Vol.1195 Mobile Robots Ⅵ,1989:168-176
[15]D.H.Croquist.Development of a Martian Surface Model for Simulation of Vehicle Dynamic and Mobility.In SPIE On Mobile Robots Ⅳ,1989:157-167
[16]R.L.Karin and O.kcith.Intersection Navigation for Unmanned Ground Vehicles.In SPIE Vol.2738,1996:14-25
[17]T.Balch and R.C.Arkin.Communication in Reactive Multiagent Robotics System.Autonomous Robots,1995,1(1):27-52
[18]C.M.Shoemaker and J.A.Bomstein.Overview of the Demo Ⅲ UGV Program.in Part of the SPIE Conf.on Robotic and Semi-Robotic Ground Vehicle Technology,1998:202-211
[19]殷波,机器人的同时定位和地图创建方法研究.2006,中国海洋大学:青岛.
[20]S.Thrun M.Montemerlo,H.Dahlkamp,et al.Stanley:The Robot That Won The DARPA Grand Challenge.Journal of Field Robotics,2006,23(9):661-692
[21]C.Urmson,C.Ragusa,D.Ray.et al.A Robust Approach to High-Speed Navigation for Unrehearsed Desert Terrain.Journal of Field Robotics,2006,23(8):467-508
[22]L.Matthies,Y.Xiong,R.Hogg,et al.A Portable,Autonomous,Urban Reconnaissance Robot.Robotics and Autonomous Systems,2002,40(2-3):163-172
[23]S.B.Goldberg,M.W.Maimone,and L.Matthies.Stereo Vision and Rover Navigation Software for Planetary Exploration.In Proceedings of the 2002 IEEE Aerospace Conference.2002,Big Sky,Montana,USA
[24]Elrob.2006[cited;Available from:http://www.elrob2006.org/
[25]刘燕军.国际军用机器人合作不断扩大.军用机器人快报,1991(10)
[26]P.Boulanger,F.Blais.Range Image Segmentation,Free Space Determination,and Position Estimate for a Mobile Vehicle.in Proc.of SPIE On Mobile Robots Ⅶ,1992:444-455
[27]T.Yiengwattanon,e.a.A Method of Autonomous Vehicle Guidance By using White Dash-line Recognition.In Proc.of 2nd Asian Conference on Computer Vision,1995
[28]王宏.运动机器人体系结构与系统设计.机器人,1993,15:49-54
[29]张朋飞,何克忠,欧阳正拄等.多功能室外智能移动机器人实验平台-THMR-V.机器人,2002,24(2):97-101.
[30]孙振平,安向京,贺汉根.CITAVT-Ⅳ—视觉导航的自主车.机器人,2002,24(2):115-121
[31]王荣本,储江伟等.一种视觉导航的实用型AGV设计.机械工程学报,2002,38(11):135-138
[32]Q.Li,N.Zheng,and H.Cheng.Springrobot:a prototype autonomous vehicle and its algorithms for lane detection.IEEE Trans.on Intelligent Transportation Systems,2004,5(4):300-308
[33]周文晖,智能机器人视觉系统研究.2005,浙江大学:杭州
[34]Roumeliontis S I,Bekey G A.Bayesian estimation and Kalman filtering a unified framework for mobile robot localization.Proceedings of the IEEE International Conference on Robotics and Automation.USA,2000:2985-2992
[35]Fox D,Burgard W,Thnm S.Markov localization for mobile robots in dynamic environments.Journal of Artificial Intelligence Research,1999,11(3):391-427
[36]Cox I,Wilfong G.Autonomous robot vehicle.London Springer-Verlag,1990:167-193
[37]Burgard W.,Fox D.,Henning D.,Schmidt T.Estimating the absolute position of a mobile robot using position probability grids.In Proceedings of the Thirteenth National Conference on Artificial Intelligence(AAAI'99),1996:896-901
[38]Burgard W.,Fox D.,Henning D.,Schmidt T.Position tracking with position probability grids.In Proceeding of the 1st Euromicro Workshop on Advanced Mobile Robots.IEEE Computer Society Press,1996
[39]Xiang Zhiyu,Liu Jilin.Initial Localization for Indoor Mobile Robots Using a 2D Laser Range Finder.In Proceeding of SPIE Vol.4573 mobile robot ⅩⅥ,Douglas W.Gage,2002:168-177
[40]Xu Zezhong,Liang Ronghua,Liu Jilin.Global localization based on comer point.In IEEE International Symposium on Computational Intelligence in Robotics and Automation,2002:843-847
[41]I.J.Cox,Blanche.An experiment in guidance and navigation of an autonomous robot vehicle.IEEE Transaction on Robotics and Automation,1991,7(2):193-204
[42]F.Lu,E.Milions.Robot pose estimation in unknown environments by matching 2D rang scans.In IEEE Int.Conf.on Computer Vision and Pattern Recongnition,1994:935-938
[43]J.S.Gutmann,C.Schlegel,AMOS.Comparison of Scan Matching Approaches for Self-localization in Indoor Environment.In Proceeding of the 1st Euromicro Workshop in Advanced Mobile Robots,1996:61-67
[44]J.Weber,K.W.Jorg,E.Von Puttkamer.APR-Global Scan Matching Using Anchor Point Relationships.In Proceeding of the 6th Int.Conf.on Intelligence Autonomous System,2000:471-478
[45]厉茂海,洪炳熔.移动机器人的概率定位方法研究进展.机器人,2005,27(4):380-384
[46]Jensfelt P.Approaches to mobile robot localization in indoor environments.Sweden Royal Institute of Technology,2001
[47]Burgard W.,Derr A,Fox D.Integrating global position estimation and position tracking for mobile robots:the dynamic Markov localization approach.In Proc.IEEE/RSJ Int.Conf.on Intelligent Robots and System,1998:730-735
[48]Qingxiang Wu,Bell DA,et al.Rough computational methods on reducing cost of computation in Markov localization for mobile robots.In Proc.the 4th World Congress on Intelligence Control and Automation,2002:1226-1230
[49]Gustafsson F.,Gunnarsson F.,Bergman N.,et al.Particle filters for positioning,navigation,and tracking.IEEE Transactions on Signal Processing,2002,50:425-435
[50]郭剑辉,赵春霞,石杏喜.基于联邦UKF算法的移动机器人自主组合导航.计算机工程与应用,2007,43(32):59-61
[51]Rudy N.Robot localization and Kalman filters.Dutch Utrecht University,2003
[52]Gutmann J.S.,Weigel T.,Nebel B.A fast,accurate,and robust method for self-localization in polygonal environments using laser range finders.Advanced Robotics Journal,2002,14(8):651-668
[53]Locchi L,Mastrantuono D,Nardi D.A probabilistic approach to Hough localization.In International Conference on Robotics and Automation,Seoul,Korea,2001
[54]Leonard J.,Durrant-Whyte H.Mobile robot localization by tracking geometric beacons.IEEE Transactions on Robotics and Automation,1991,7(3):376-382
[55]Jensfelt P.,Christensen H.I.Laser based pose tracking.In proceedings of IEEE International Conference on Robotics and Automation,1999:2994-3000
[56]Wada M.,Yoon KS,Hashimoto H.High accuracy road vehicle state estimation using extended Kalman filter.In Proceedings of 3rd IEEE International Conference on Intelligent Transportation Systems,2000:282-287
[57]Fox D.Markov localization:A Probabilistic framework for mobile robot localization and navigation.German:University of Bonn,1998
[58]Thrun S.,Fox D.,Burgard W,et al.Robust Monte Carlo localization for mobile robot.Artificial Intelligence,2001,128(1-2):99-141
[59]Julier S.J,Uhlman J.k,Durrant-Whyte H.A new approach for the nonlinear transformation of means and covariance in linear filters.IEEE Transaction on Automatic Control,2000,5:477-482
[60]Ito K.,Xiong K.Gaussian for nonlinear filtering problems.IEEE Transaction on Automatic Control,2000,45(5):910-927
[61]Alspach D,Sorenson.Nonlinear Bayesian estimation using Gaussian sum approximations.IEEE Transaction on Automatic Control,1972,AC(17):439-448
[62]Sorenson,Alspach D.Recursive Bayesian estimation using Gaussian sum Automatic,1971,7(2):465-479
[63]T.Bailey.Mobile Robot Localisation and Mapping in Extensive Outdoor Environments.PHD thesis,Australian Centre for Field Robotics,Department of Aerospace,Mechanical and Mechatronic Engineering,University of Sydney,August 2002
[64]T.Bailey,E.Nebot,J.Rosenblatt,and H.Durrant-Whyte.Robust Distinctive Place Recognition for Topological Maps.In Proceedings of International Conference on Field and Service Robotics.August 1999,Pittsburgh,PA,USA
[65]B.Kuipers,Y.Byun.A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations.IEEE Journal of Robotics and Autonomous System, 1991,8:47-63
[66]A.Elfes.Occupancy Grids: A Stochastic Spatial Representation for Active Robot Perception.In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence.July 1990
[67]M.Martin, H.Moravec.Robot Evidence Grids.Technical Report: CMURI-TR-96-06, Carnegie Mellon University Robotics Institute, Pittsburgh, PA 15213,March 1997
[68]M.Ribo, A.Pinz.A Comparison of Three Uncertainty Calculi for Building Sonar-based Occupancy Grids.IEEE Journal of Robotics and Autonomous Systems,2001,35 :201-209
[69]S.Thrun.Learning Occupancy Grids with Forward Models.In Proceedings of the IEEE/RSJ International Conference on Intelligent Robotics and Systems, Maui, Hawaii, USA,2001:1676-1681
[70]H.Moravec, A.Elfes.High-resolution Maps from Wide-angle Sonar.In Proceedings of the IEEE International Conference on Robotics and Automation.M arch 1985:116-121
[71]A.Amir, A.Efrat, P.Indyk, H.Samet.Efficient Regular Data Structures and Algorithm for Location and Proximity Problems.In IEEE Symposium on Foundations of Computer Science, 1999:160-170
[72]K.Chong, L.Kleeman.Mobile robot map building from an advanced sonar array and accurate odometry.Intl.Journal of Robotics Research, 1999,1 8(1):20-36
[73]J.J.Leonard,H.F.Durrant-Whyte, I J.Cox.Dynamic map building for an autonomous mobile robot.Intl.Journal of Robotics Research, 1992,ll(4):286-298
[74]0.Aycard, F.Charpillet, D.Fohr, J.Mari.Place Learning and Recognition Using Hidden Markov Models.In Proceedings of the IEEE International Conference on Intelligent Robots and Systems,1997: 1741-1746
[75]R.Simmons, S.Koenig.Probabilistic Robot Navigation in Partially Observable Environments.International Joint Conference on Artificial Intelligence, 1995: 1080-1087
[76]S.Thrun.Learning Metric-Topological Maps for Indoor Mobile Robot Navigation.Artificial Intelligence, 1998, 99(1):21-27
[77]B.Yamauchi, P.Langley.Place recognition in dynamic environments.Journal of Robotic Systems,1997, 14(2):107-120
[78]S.Simhon, G.Dudek.A global topological map formed by local metric maps.In IEEE/RSJ International Conference on Intelligent Robots and Systems, 1998, 1708-1714
[79]K.S.Chong and L.Kleeman.Feature-based mapping in real, large scale environments using an ultrasonic array.International Journal Robotics Research, 1999, 18(1):3-19
[80]H.Choset and K.Nagatani.Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization.IEEE Transactions on Robotics and Automation,2001, 17(2):125-137
[81]J.A.Castellanos, J.M.M.Montiel, J.Neira, and J.D.Tard'os.The SPmap: A probabilistic framework for simultaneous localization and map building.IEEE Trans, on Robotics and Automation,1999,15(5):948-952
[82]J.Guivant and E.Nebot.Compressed filter for real time implementation of simultaneous localization and map building.In Aarne Halme, Raja Chatila, and Erwin Prassler, editors, Int.Conf.on Field and Service Robots, 2001:309-314
[83]J.J.Leonard, R.Carpenter, and H.J.S.Feder.Stochatic mapping using forward look sonar.Robotica,2001,19:467-480
[84]J.H.Kim and S.Sukkarieh.Airborne simultaneous localisation and map buildingfC].In IEEE Int.Conf.on Robotics and Automation, Taipei,Taiwan, September 2003
[85]R.Smith and P.Cheesman.On the representation of spatial uncertainty.Int.J.Robot.Research, 1987,5(4):56-68
[86]H.F.Durrant-Whyte.Uncertain geometry in robotics.IEEE Trans.Robot.Automation,1988,4(1):23-31
[87]N.Ayache and 0.Faugeras.Building, registrating, and fusing noisy visual maps.Int.J.Robot.Research,1988,7(6): 45-65
[88]J.Crowley.World modeling and position estimation for a mobile robot using ultra-sonic ranging.In Proc.IEEE Int.Conf.Robotics and Automation, 1989 :674-681
[89]R.Chatila and J.P.Laumond.Position referencing and consistent world modeling for mobile robots.In Proc.IEEE Int.Conf.Robotics and Automation, 1985:138-143
[90]R.Smith, M.Self, and P.Cheeseman.Estimating uncertain spatial relationships in robotics.In I.J.Cox and G.T.Wilfon, editors, Autonomous Robot Vehicles, pages 167-193.Springer-Verlag, 1990
[91]J.J.Leonard and H.F.Durrant-Whyte.Directed Sonar Navigation.Kluwer Academic Press, 1992
[92]W.D.Renken.Concurrent localization and map building for mobile robots using ultrasonic sensors.In Proc.IEEE Int.Workshop on Intelligent Robots and Systems (IROS), 1993
[93]H.Durrant-Whyte, D.Rye, and E.Nebot.Localization of automatic guided vehicles.In G.Giralt and G Hirzinger, editors, Robotics Research: The 7th International Symposium (ISRR'95),pages 613-625.Springer Verlag, 1996
[94]M.Csorba.Simultaneous Localization and Map Building.PhD thesis, University of Oxford, 1997
[95]M.Csorba and H.F.Durrant-Whyte.A new approach to simultaneous localization and map building.In Proceedings of SPIE Aerosense, Orlando, 1996
[96]J.J.Leonard and H.J.S.Feder.A computational efficient method for large-scale concurrent mapping and localisation.In J.Hollerbach and D.Koditscheck, editors, Robotics Research, The Ninth International Symposium (ISRR'99), pages 169-176.Springer Verlag, 2000
[97]J.A.Castellanos, J.M.Martnez, J.Neira, and J.D.Tardos.Experiments in multisensor mobile robot localization and map building.In Third IFAC Symposium on Intelligent Autonomous Vehicles,1998:173-178
[98]J.A.Castellanos, J.D.Tardos, and G.Schmidt.Building a global map of the environment of a mobile robot: The importance of correlations.In IEEE International Conference on Robotics and Automation,1997:1053-1059
[99]J.Guivant, E.M.Nebot, and S.Baiker.Localization and map building using laser range sensors in outdoor applications.Journal of Robotic Systems, 2000,17(10):565-583
[100]S.B.Williams, P.Newman, GDissanayake, and H.F.Durrant-Whyte.Autonomous underwater simultaneous localisation and map building.In Proc.IEEE International Conference on Robotics and Automation (ICRA), pages 1793-1798, San Francisco, USA, April 2000
[101]T.Bailey, J.Nieto, J.Guivant, M.Stevens, and E.Nebot.Consistency of the EKF-SLAM algorithm.IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006: 3562-3568
[102]M.Deans and M.Hebert.Experimental comparison of techniques for localization and mapping using a bearing-only sensor.In Int.Symp.Experimental Robotics, 2000
[103]J.Hollerbach and D.Koditscheck (Editors).Robotics Research, The Ninth International Symposium (ISRR'99).Springer Verlag, 2000
[104]S.Thrun, D.Fox, and W.Burgard.A probabilistic approach to concurrent mapping and localization for mobile robots.Machine Learning, 1998, 31(1):29-53
[105]Durrant-Whyte, H., Bailey, T.Simultaneous localization and mapping: part I.IEEE Robotics and Automation Magazine, September 2006, 13(2):99-110
[106]Bailey, T., Durrant-Whyte, H.Simultaneous localization and mapping: part EL IEEE Robotics and Automation Magazine, September 2006,13(3): 108-117
[107]J.Guivant and E.Nebot.Optimization of the simultaneous localization and map building algorithm for real time implementation.IEEE Transactions on Robotics and Automation, 2001, 17(3):242-257
[108]J.Knight, A.Davison, and I.Reid.Towards constant time SLAM using postponement.In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001:405-413
[109]S.B.Williams.Efficient Solutions to Autonomous Mapping and Navigation Problems.PhD thesis, University of Sydney, Australian Centre for Field Robotics, 2001.
[110]J.D.Tard'os, J.Neira, P.M.Newman, and J.J.Leonard.Robust mapping and localization in indoor environments using sonar data.International Journal of Robotics Research, 2002,21(4):311-330
[111]S.Thrun, D.Koller, Z.Ghahmarani, and H.Durrant-Whyte.SLAM updates require constant time.School of Computer Science, Carnegie Mellon University, Pittsburgh, Technical Report, 2002
[112]S.Thrun, Y.Liu, D.Koller, A.Ng, Z.Ghahramani, and H.Durrant-Whyte.Simultaneous localization and mapping with sparse extended information filters.International Journal of Robotics Research,July-August 2004, 23(7-8):693-716
[113]M.Paskin.Thin junction tree filters for simultaneous localization and mapping.Proceedings of the 18th International Joint Conference on Artificial Intelligence, San Francisco, 2003:1157-1164
[114]U.Frese.An 0(log n) algorithm for simultaneous localization and mapping of mobile robots in indoor environments.Ph.D dissertation, University of Erlangen-Nurnberg, 2004
[115]Y.Liu and S.Thrun.Results for Outdoor-SLAM Using Sparse Extended Information Filters.IEEE International Conference on Robotics and Automation, vol.1, September 2003: 1227-1233
[116]U.Frese.A proof for the approximate sparsity of SLAM information matrices .Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, April 2005
[117]R.Eustice, M.Walter, and J.Leonard.Sparse extended information filters: Insights into sparsification.Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton,Alberta, Canada, August 2005
[118]M.Walter, R.Eustice, and J.Leonard.A provably consistent method for imposing sparsity in feature-based SLAM information filters.Intl.Symp.on Robotics Research, October 2005
[119]M.R.Walter, R.M.Eustice, and J.J.Leonard.Exactly sparse extended information filters for feature-based SLAM, Intl.J.Robotics Research, April 2007,26 (4): 335-359
[120]F.Dellaert, Michacel Kaess.Square root SAM: Simultaneous location and mapping via square root information smoothing.International Journal of Robotics Research, Dec.2006,25(12):1181-1203
[121]R.M.Eustice, H.Singh, J.J.Leonard.Exactly Sparse Delayed-State Filters for View-Based SLAM.IEEE Transaction on Robotics, Dec.2006,22(6):1100-1114
[122]J.Folkesson and H.I.Christensen.Graphical SLAM - a self correcting map.In IEEE International Conference on Robotics and Automation, 2004 : 791-798
[123]Z.Wang, S.Huang, and G.Dissanayake.Implementation issues and experimental evaluation of D-SLAM.In International Conference on Field and Service Robotics, 2005
[124]R.Eustice, H.Singh, J.Leonard, M.Walter, and R.Ballard.Visually navigating the RMS Titanic with SLAM information filters.In Robotics: Science and Systems, 2005
[125]J.Guivant and E.Nebot.Improving computational and memory requirements of simultaneous localization and map building algorithms.In IEEE International Conference on Robotics and Automation, 2002: 2731-2736
[126]J.Leonard and P.Newman.Consistent, convergent, and constant-time SLAM.In International Joint Conference on Artificial Intelligence, 2003
[127]S.J.Julier and J.K.Uhlmann.A non-divergent estimation algorithm in the presence of unknown correlations.In American Control Conference, volume 4,1997: 2369-2373
[128]M.Bosse, P.Newman, J.Leonard, M.Soika, W.Feiten, and S.Teller.An Atlas framework for scalable mapping.In IEEE International Conference on Robotics and Automation, 2003, 1899-1906
[129]M.Bosse, P.Newman, J.Leonard, and S.Teller.Simultaneous localization and map building in large-scale cyclic environments using the Atlas framework.International Journal of Robotics Research,2004,23(12):1113-1140
[130]C.Estrada, J.Neira, and J.D.Tardos.Hierarchical SLAM: Realtime accurate mapping of large environments.IEEE Transactions on Robotics, 2005,21(4):588-596
[[131]Sen Zhang, Lihua Xie, Adams M.An efficient data association approach to simultaneous localization and map building.IEEE International Conference on Robotics and Automation, 2004, Volume 1,Page(s):854 -859
[132]W.Sardha Wijesoma, L.D.L.Perera, M.D.Adams.Toward Multidimensional Assignment Data Association in Robot Localization and Mapping.IEEE Transactions on Robotics, April 2006,22(2):350-365
[133]J.Neira and J.D.Tardos.Data association in stochastic mapping using the joint compatibility test.IEEE Trans.Robot.Autom, Dec.2001,17(6): 890-897
[134]Y.Bar-Shalom and T.E.Fortmann.Tracking and Data Association.Boston, MA: Academic, 1988.
[135]I.J.Cox and J.J.Leonard.Modeling a dynamic environment using a bayesian multiple hypothesis approach.Artificial Intelligence, 1994,66(2): 311-344
[136]J.Nieto, J.Guivant, E.Nebot, and S.Thran.Real time data association for FastSLAM.IEEE Int.Conf.Robot.Autom., Taipei,Taiwan, R.O.C., Sep.14-19,2003:412-418
[137]M.Montemerlo and S.Thran.Simultaneous localization and mapping with unknown data association using FastSLAM.In IEEE International Conference on Robotics and Automation, 2003: 1985-1991
[138]J.Neira, J.D.Tard'os, and J.A.Castellanos.Linear time vehicle relocation in SLAM.IEEE Int.Conf.on Robotics and Automation, Taipei, Taiwan, September 2003:427-433
[139]J.J.Leonard and R.J.Rikoski.Incorporation of delayed decision making into stochastic mapping.In International Symposium On Experimental Robotics, 2000
[140]J.J.Leonard, R.J.Rikoski, P.M.Newman, and M.C.Bosse.Mapping partially observable features from multiple uncertain vantage points.International Journal of Robotics Research, 2002, 21(10-ll):943-975
[141]T.Bailey.Constrained initialisation for bearing-only SLAM.In IEEE International Conference on Robotics and Automation, 2003
[142]J.Sofa, A.Monin, M.Devy, and T.Lemaire.Undelayed initialization in bearing only SLAM.In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005: 2751-2756
[143]J.Nieto, T.Bailey, and E.Nebot.Scan-SLAM: Combining EKFSLAM and scan correlation.In International Conference on Field and Service Robotics, 2005
[144]A.J.Davison, Y.G.Cid, and N.Kita.Real-time 3D SLAM with wide-angle vision.In IFAC/EURON Symposium on Intelligent Autonomous Vehicles, 2004
[145]P.Newman, D.Cole, and K.Ho.Outdoor SLAM using visual appearance and laser ranging.In IEEE International Conference on Robotics and Automation, 2006:1180-1187
[146]K.Konolige.Large-scale map-making.In National Conference on AI (AAAI), 2004: 457-463
[147]D.Hahnel, W.Burgard, D.Fox, and S.Thran.An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements.In IEEE/RSJ International Conference on Intelligent Robots and Systems,2003:206-211
[148]A.I.Eliazar and R.Parr.DP-SLAM 2.0.In IEEE International Conference on Robotics and Automation,2004:1314-1320
[149]G.Grisetti,C.Stachrtiss,and W.Burgard.Improving gridbased SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling.In IEEE International Conference on Robotics and Automation,2005:667-672
[150]D.Hahnel,R.Triebel,W.Burgard,and S.Thrun.Map building with mobile robots in dynamic environments.In IEEE International Conference on Robotics and Automation,2003:1557-1563
[151]C.C.Wang,C.Thorpe,and S.Thrun.On-line simultaneous localisation and mapping with detection and tracking of moving objects.In IEEE International Conference on Robotics and Automation,2003:2918-2924
[152]罗荣华,洪炳铭.基于信息融合的同时定位与地图创建研究.哈尔滨工业大学学报,2004,36(5):56-569
[153]G Dissanayake,P Newman,H F Durrant-Whyte,S Clark,M Csobra.A solution to the simultaneous localisation and map-maping(SLAM) problem.IEEE Trans.Robotics and Automation,2001,17(3):229-241
[154]H F Durrant-Whyte,G Dissanayake,P W Gibbens.Toward deployment of large-scale simultaneous localization and map building(SLAM) system.The Ninth Int.Symposium on Robotics Research.Lorne,Australia:ISRR press,2001:229-241
[155]S.Julier,J.Uhlmann,and H.F.Durrant-Whyte.A new method for the nonlinear transformation of means and covariances in filters and estimators.IEEE Trans.Automat.Contr.,2000,45(3):477-482
[156]S.J.Julier,and J.K.Uhlmann.The Scaled Unscented Transformation.In IEEE American Control Conference,Anchorage AK,USA,8-10 May 2002:4555-4559
[157]S.J.Julier and J.K.Uhlmann.Unscented filtering and nonlinear estimation.Proceedings of the IEEE,2004,92(3):401-422
[158]Juan Andrade-Cetto,Teresa Vidal-Calleja,and Alberto Sanfeliu.Unscented transformation of vehicle states in slam.In Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 05),Barcelona,Spain,18-20 April 2005:324-329
[159]Y.Bar-Shalom,X.Rong Li,and T.Kirubarajan.Estimation with Applications to Tracking and Navigation[M].Wiley InterScience,2001
[160]S.J.Julier and J.K.Uhlmann.A counter example to the theory of simultaneous localization and map building.IEEE International Conference on Robotics and Automation,2001:4238-4243
[161]J.A.Castellanos,J.Neira,and J.D.Tardos.Limits to the consistency of EKF-based SLAM.IFAC Symposium on Intelligent Autonomous Vehicles,2004
[162]S.Huang and G.Dissanayake.Convergence analysis for extended Kalman filter based SLAM.IEEE International Conference on Robotics and Automation,2006
[163]K.Murphy.Bayesian map learning in dynamic environments.Proc.of the Conf.on Neural Information Processing Systems,NIPS,Denver,CO,USA,1999:1015-1021
[164]A.Doucet,J.de Freitas,K.Murphy,S.Russel.RaoBlackwellizedparticle filtering for dynamic Bayesian networks.Proc.of the Conf.on Uncertainty in Artificial Intelligence,UAI,Stanford,CA,USA,2000:176-183
[165]M.Montemerlo,S.T.D.Koller,B.Wegbreit.FastSLAM 2.0:An improved particle filtering algorithm for simultaneous localization and mapping that provably converges.Proc.of the Int.Conf.on Artificial Intelligence,IJCAI,Acapulco,Mexico,2003:1151-1156
[166]M.Montemerlo,S.Thrun,D.Koller,B.Wegbreit.FastSLAM:A factored solution to simultaneous localization and mapping.Proc.of the National Conference on Artificial Intelligence,AAAI,Edmonton,Canada,2002:593-598
[167]厉茂海,洪炳熔,罗荣华.用改进的Rao-Blackwellized粒子滤波器实现移动机器人同时定位和地图创建.吉林大学学报(工学版),2007,37(2):401-406
[168]余洪山,王耀南.基于粒子滤波器的移动机器人定位和地图创建研究进展.机器人,2007,29(3):281-289
[169]黄庆成,洪炳熔,厉茂海,罗荣华.基于主动环形闭合约束的移动机器人分层同时定位和地图创建.计算机研究与发展,2007,44(4):636-642
[170]N.Gordon,D.Salmond,A.F.M.Smith.Novel Approach to Non-linear and Non-Gaussian Bayesian State Estimation.Proc of Institute Electric Engineering,1993,140(2):107-113
[171]Sanjeev Arulampalam,Simon Maskell,Neil Gordon,Tim Clapp.A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking.IEEE Trans Signal Proceeding,2002:174-188
[172]M.Pitt,N.Shephard.Filtering via simulation:Auxiliary particle filters.Journal of the American Statistical Association,1999,94(446):590-599
[173]C.Musso,N.Oudjane,F.LeGland.Improving Regnlafised particle filters.Sequential Monte Carlo Methods in practicle,eds A.Doucet,J.F.G.de Freitas and N.J.Gordon.2001,Springer-verlag,New York
[174]M.Klaas,N.de Freitas,and A.Doucet.Toward practical n2 Monte Carlo:The marginal particle filter.Proceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence(UAI-05).Arlington,Virginia:AUAI Press,2005,308-315
[175]D Lang,MKlaas,and N de Freitas.Empirical testing of fast kernel density estimation algorithms.Department of Computer Science,UBC,Technical Report:TR-2005-03,2005
[176]Ryan Eustice,Hanumant Singh,and John Leonard.Exactly Sparse Delayed-State Filters.Proceedings of the 2005 IEEE International Conference on Robotics and Automation,April 2005:2428-2435
[177]J.H.Lim and J.J.Leonard.Mobile robot relocation from echolocation constraints.IEEE Trans.Pattern Analysis and Machine Intelligence,September 2000,22(9):1035-1041
[178]W.E.L.Grimson.Object Recognition by Computer:The Role of Geometric Constraints.The MIT Press,Cambridge,Mass.,1990
[179]T.Bailey,E.M.Nebot,J.K.Rosenblatt,and H.F.Durrant-Whyte.Data association for mobile robot navigation:A graph theoretic approach.IEEE Int.Conf.Robotics and Automation,San Francisco,California,2000:2512-2517
[180]D.Fox,W.Burgard,D.F.,and S.Thrun.Monte-Carlo localization:Efficient position estimation for mobile robots.Proc.of the Sixteenth National Conference on Artificial Intelligence(AAAI-99),Orlando,Florida,1999:343-349
[181]D.Fox,W.Burgard,and S.Thrun.Active markov localization for mobile robots.Robotics and Autonomous Systems,1998,25(3-4):195-207