室内自主移动机器人的定位研究
|
摘要
定位是自主移动机器人的基本功能,也是移动机器人实现自主导航的关键,对于提高机器人自动化水平具有重要意义。论文对移动机器人的绝对定位技术进行了深入分析和系统研究。
首先,论文将无线传感器网络引入移动机器人的研究中,提出了一种基于无线传感器网络ZigBee的定位方法,该方法类似于一个小型的卫星定位系统(GPS)。在不能或不宜使用卫星系统进行定位时,可以用该方法代替卫星系统对移动机器人进行定位。
其次,在回顾和总结目前存在的移动机器人环境建模方法的基础上,本文提出了一种新的基于二维激光测距传感器的几何地图构建方法:即采用线段和特征点描述环境。对环境的特征提取方法进行了详细的阐述,从而建立了比较完整的环境地图。
再次,在环境模型建立的基础上,论文提出一种基于完整线段匹配的定位算法:即基于完整线段的长度关系建立匹配假设,根据完整线段与特征点的相对位置关系对匹配假设进行评价,进而建立匹配矩阵和标志矩阵,根据匹配矩阵和标志矩阵,利用最佳匹配搜索算法搜索最佳匹配,基于最佳匹配,得到机器人的位姿。
最后,论文将无线传感器网络ZigBee信息和激光测距传感器信息相融合,提出了室内移动机器人快速定位算法。利用无线传感器网络ZigBee实现移动机器人的粗略定位,然后从先验全局地图中抽取该位置邻近的参考地图,再与从激光测距传感器获取的信息中快速提取的环境特征进行匹配,根据匹配的环境特征实现机器人的精确定位。
Localization is the basic function and the critical step towards autonomous navigation of autonomous mobile robot. It is of great significance to improve the automatization level. In this dissertation, a deep analysis and systematic study of technologies related to the mobile robot’s absolute positioning are carried on.
Firstly, a wireless sensor networks is introduced into the research of mobile robot. This paper presents a localization method based on the ZigBee wireless sensor networks. This method is similar to a small global positioning system (GPS), and it can be used in place of satellite systems for mobile robot localization while which can not or should not be used.
Secondly, on the basis of review and summary of methods on mobile robot environmental model, this paper proposes a new geometry map building approach based on two-dimensional laser range sensor, namely, line segment and feature point are adopted to describe environment. The method of environmental feature extraction is described in detail in order to build a relatively complete map of the environment.
Thirdly, this paper presents a localization method on the basis of complete line segments matching when the environmental model is established. The paper establishes match assumptions based on the length of complete line segments, and evaluates the match assumptions using the relative relationships between complete line segments and feature points in order to establish the match matrix and the flag matrix. The robot pose is obtained based on the best match which is searched according to the match matrix and flag matrix using the best match search algorithm.
Finally, the paper presents a rapid localization method for an indoor mobile robot using the integrated information from the ZigBee wireless sensor networks and laser range sensor. The algorithm achieves a rough global location of the mobile robot by using the ZigBee wireless sensor networks, and extracts the reference map close to the mobile robot from the known global map. Then, the algorithm matches the known global map and the environmental features extracted from the laser range sensor information. According to the matched environmental characteristics, the accurate position of the robot can be obtained.
首先,论文将无线传感器网络引入移动机器人的研究中,提出了一种基于无线传感器网络ZigBee的定位方法,该方法类似于一个小型的卫星定位系统(GPS)。在不能或不宜使用卫星系统进行定位时,可以用该方法代替卫星系统对移动机器人进行定位。
其次,在回顾和总结目前存在的移动机器人环境建模方法的基础上,本文提出了一种新的基于二维激光测距传感器的几何地图构建方法:即采用线段和特征点描述环境。对环境的特征提取方法进行了详细的阐述,从而建立了比较完整的环境地图。
再次,在环境模型建立的基础上,论文提出一种基于完整线段匹配的定位算法:即基于完整线段的长度关系建立匹配假设,根据完整线段与特征点的相对位置关系对匹配假设进行评价,进而建立匹配矩阵和标志矩阵,根据匹配矩阵和标志矩阵,利用最佳匹配搜索算法搜索最佳匹配,基于最佳匹配,得到机器人的位姿。
最后,论文将无线传感器网络ZigBee信息和激光测距传感器信息相融合,提出了室内移动机器人快速定位算法。利用无线传感器网络ZigBee实现移动机器人的粗略定位,然后从先验全局地图中抽取该位置邻近的参考地图,再与从激光测距传感器获取的信息中快速提取的环境特征进行匹配,根据匹配的环境特征实现机器人的精确定位。
Localization is the basic function and the critical step towards autonomous navigation of autonomous mobile robot. It is of great significance to improve the automatization level. In this dissertation, a deep analysis and systematic study of technologies related to the mobile robot’s absolute positioning are carried on.
Firstly, a wireless sensor networks is introduced into the research of mobile robot. This paper presents a localization method based on the ZigBee wireless sensor networks. This method is similar to a small global positioning system (GPS), and it can be used in place of satellite systems for mobile robot localization while which can not or should not be used.
Secondly, on the basis of review and summary of methods on mobile robot environmental model, this paper proposes a new geometry map building approach based on two-dimensional laser range sensor, namely, line segment and feature point are adopted to describe environment. The method of environmental feature extraction is described in detail in order to build a relatively complete map of the environment.
Thirdly, this paper presents a localization method on the basis of complete line segments matching when the environmental model is established. The paper establishes match assumptions based on the length of complete line segments, and evaluates the match assumptions using the relative relationships between complete line segments and feature points in order to establish the match matrix and the flag matrix. The robot pose is obtained based on the best match which is searched according to the match matrix and flag matrix using the best match search algorithm.
Finally, the paper presents a rapid localization method for an indoor mobile robot using the integrated information from the ZigBee wireless sensor networks and laser range sensor. The algorithm achieves a rough global location of the mobile robot by using the ZigBee wireless sensor networks, and extracts the reference map close to the mobile robot from the known global map. Then, the algorithm matches the known global map and the environmental features extracted from the laser range sensor information. According to the matched environmental characteristics, the accurate position of the robot can be obtained.
引文
1蔡自兴,贺汉根,陈虹.未知环境中移动机器人导航控制研究的若干问题.控制与决策, 2002, 17(4): 385-390
2郑向阳,熊蓉,顾大强.移动机器人导航和定位技术.机电工程, 2003, 20(5): 35-37
3李磊,叶涛,谭民,等.移动机器人技术研究现状与未来.机器人, 2002, 24(5): 475-480
4徐国华,谭民.移动机器人的发展现状及其趋势.机器人技术与应用, 2001, 3: 7-13
5朱淼良,杨建刚,吴春明.自主式智能系统.浙江:浙江大学出版社, 2000: 1-155
6 G.N. Saridis. Toward the Realization of Intelligent Controls. Proceedings of the IEEE, 1979, 67(8): 1115-1133
7 R. Brooks. A Robust Layered Control System for a Mobile Robot. Journal of Robotics and Automation, 1986, 2(l): 14-23.
8 H.F. Durrant-Whyte. Where am I? A Tutorial on Mobile Vehicle Localization. Industrial Robot, 1994, 21(2): 11-16
9王挺,王越超.人机系统在移动机器人平台的应用.机器人, 2004, 26(6): 553-557
10王立娜.视觉临场感遥控系统在移动机器人中的应用.中国科技信息, 2007, 19: 265-266
11 G.A. Borges, M.J. Aldon. Optimal Mobile Robot Pose Estimation Using Geometrical Maps. Proceedings of the IEEE Transaction on Robotics and Automation, 2002, 1: 87-93
12 S.Y. Kim, S.H. Park. Estimation of Absolute Positioning of Mobile Robot Using U-STA. Lecture Notes in Computer Science, 2008, 4672: 143-150
13 L. Moreno, et al. A Genetic Algorithm for Mobile Robot Localization Using Ultrasonic Sensors. Journal of Intelligence and Robotics Systems, 2002, 34: 135-154
14杨明,王宏,张钹.基于激光雷达的移动机器人位姿估计方法综述.机器人, 2002, 24(2): 177-183
15 W. Burgard, et al. Estimation the Absolute Position of a Mobile Robot Using Position Probability Grids. Proceedings of the National Conference on Artificial Intelligence. Portland, Oregon, USA, 1996: 896-901
16张毅,罗元,郑太雄,等.移动机器人技术及其应用.北京:电子工业出版社, 2007: 139-165
17 D. Fox, W. Burgard, F. Dellaert, et al. Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. Proceedings of the National Conference on Artificial Intelligence, 1999: 343-349
18 S. Thrun, D. Fox, W. Burgard, et al. Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 2002, 128: 99-141
19 C. Gonzalez-Buesa, J. Campos. Solving the Mobile Robot Localization Problem Using String Matching Algorithms. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, 3(28): 2475-2480
20孟庆杰,董绪荣. GPS/DR组合导航系统故障探测与剔除(FDI)的容错设计.装备指挥技术学院学报, 2002, 13(3): 40-43
21郭圣权,李杰.车辆兵器GPS/DR/DM组合导航系统的研究.火力与指挥控制, 2002, 27(5): 13-16
22 G. Jang, S. Kim, W. Lee and I. Kweon. Robust Self-localization of Mobile Robots Using Artificial and Natural Landmarks. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, 2003, 1: 412-417
23孟凯,孙茂相,孙金根.基于主动路标的全方位移动机器人定位系统.沈阳工业大学学报, 2000, 22(2): 148-151
24 O. Wijk, H.I. Christensen. Localization and Navigation of a Mobile Robot Using Natural Point Landmarks Extracted from Data. Robotics and Autonomous Systems, 2000, 3: 131-142
25唐琏,白涛,蔡自兴.移动机器人的一种室内自然路标定位法.计算机工程与应用, 2005, 4(15): 44-47
26 V.T. Nguyen, M.S. Jeong, S.M. Ahn, et al. A Robust Localization Method for MobileRobots Based on Ceiling Landmarks. Lecture Notes in Computer Science, 2007, 4617: 422-430
27 Hongbo Wang, Lingfu Kong and Hongnian Yu. Ceiling Light Landmarks Based Localization and Motion Control for a Mobile Robot. Proceedings of the IEEE International Conference on Networking, Sensing and Control. London, April 15-17, 2007: 285-290.
28 R. Simmon, S. Koening. Probabilistic Navigation in Partially Observable Environments. Proceedings of the International Joint Conference on Artificial Intelligence, 1995: 1080-1087
29 Xu Zezhong, Liu Jilin and Xiang Zhiyu. Scan Matching Based on CLS Relationships. Proceedings of IEEE International Conference on Robotics, Intelligent Systems and Signal Processings, Changsha, China, 2003: 99-104.
30王志文,郭戈.移动机器人导航技术现状与展望.机器人, 2003, 25(5): 470-474
31 J.L. Blanco, J.A. Fernández-Madrigal and J. Gonzalez. A Novel Measure of Uncertainty for Mobile Robot SLAM with Rao-Blackwellized Particle Filters. The International Journal of Robotics Research, 2008, 27(1): 73-89
32 D.F. Wolf, G.S. Sukhatme. Online Simultaneous Localization and Mapping in Dynamic Environments. Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, 2004: 1301-1306
33 D.F. Wolf, G.S. Sukhatme. Mobile Robot Simultaneous Localization and Mapping in Dynamic Environment. Autonomous Robots, 2005, 19: 53-65
34 D. H?hnel, R. Triebel, W. Burgard, et al. Map Building with Mobile Robots in Dynamic Environment. Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, 2003: 1557-1563
35 G.Q. Huang, A.B. Rad and Y.K. Wong. A New Solution to Map Dynamic Indoor Environments. International Journal of Advanced Robotic System, 2006, 3(2): 199-210
36 C. Stachniss, W. Burgard. Mobile Robot Mapping and Localization in Non-static Environments. Proceeding of the International Conference on Artificial Intelligence.Pensylvania, 2005: 1324-1329
37 M. Montemerlo, S. Thrun. Fast SLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. Proceedings of the 18th National Conference on Artificial Intelligence, 2002: 593-598
38 J.A. Byrne. 21 Ideas for the 2lst Century. Business Week, Aug.30, 1999: 78-167
39 Yuqing Gao, Ron Weiss, Peidong Yang, et al. 10 Emerging Technologies That Will Change Your World. Technology Review, February, 2004, 106(l): 32-50
40李文仲,段朝玉. ZigBee2006无线网络与无线定位实战.北京:北京航空航天大学出版社, 2008: 1-382
41 P. Baronti, P. Pillai, V. Chook, et al. Wireless Sensor Networks: A Survey on the State of the Art and the 802.15.4 and ZigBee Standards. Computer Communications, 2007: 1655-1695
42李响,王川,刘序文,等.基于ZigBee的无线传感器网络定位系统的研究.中国科技论文在线, http://www.paper.edu.cn
43段渭军,王建刚,王福豹.无线传感器网络节点定位系统与算法的研究和发展.信息与控制, 2006, 35(2): 239-245
44 T. He, C.D. Huang and B.M. Blum. Range-free Localization Schemes for Large Scale Sensor Networks. Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, San Diego, California, USA, 2003: 81-95
45 N. Bulusu, J. Heidemann and D. Estrin. GPS-less Low Cost Outdoor Localization for Very Small Devices. Personal Communications, 2000, 7(5): 28-34
46 D. Niculescu, B. Nath. DV-based Positioning in Ad Hoc Networks. Journal of Telecommunication Systems, 2003, 22(1/4): 267-280
47傅明磊,董慧颖.无线传感器网络节点定位技术研究.科技咨询导报, 2007, 3: 28-29
48 L. Doherty, K.S.J. Pister and L.E. Ghaoui. Convex Position Estimation in Wireless Sensor Networks. Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies(INFOCOM), Anchorage, AK, USA: 2001, 3: 1655-1663
49 Y. Shang, W. Ruml, Y. Zhang and M. Fromherz. Localization from Mere Connectivity. Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking & computing, Annapolis, Maryland, USA, 2003: 201-212
50章浩,李萍萍,张西良.无线传感器网络节点定位机制研究.宽带网络与传输, 2006, 10: 70-73
51周正.无线传感器网络的节点自定位技术.中兴通讯技术, 2005, 11(4): 51-56
52 Chipcon, http://www.chipcon.com/files/CC2431_Location_Engine. Pdf, 2006
53宋文,无线传感器网络技术与应用.北京:电子工业出版社, 2007: 105-106
54陈维克,李文锋,首珩,等.基于RSSI的无线传感器网络加权质心定位算法.武汉理工大学学报(交通科学与工程版), 2006, 30(2): 265-268
55 D. Taubenheim, S. Kyperountas and N. Correal. Distributed Radiolocation Hardware Core for IEEE 802.15.4. http://www.chipcon.com/files/Radioloeation_Engine_WP. Pdf, 2005
56 D. Kortenkamp, T. Weynouth. Topological Mapping for Mobile Robots Using a Combination of Sonar and Vision Sensing. Proceeding of the 12th National Conference on Artificial Intelligence, Menlo Park, 2004, 2: 979-984
57 S. Thrun, A. Bücken. Integrating Grid-based and Topological Maps for Mobile Robot Navigation. Proceedings of 13th National Conference on Artificial Intelligence, Portland Oergon, 1996, 2: 944-950
58李云翀,何克忠.基于激光雷达的室外移动机器人避障与导航新方法.机器人, 2006, 28(3): 275-278
59蔡自兴,郑敏捷,邹小兵.基于激光雷达的移动机器人实时避障策略.中南大学学报(自然科学版), 2006, 37(2): 324-329
60 Y. Cang, J. Borenstein. Characterization of a 2-D Laser Scanner for Mobile Robot Obstacle Navigation. Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC, 2002, 3: 2512-2518
61 J.X. Yu, Z.X. Cai and Z.H. Duan. Mobile Robot Self-localization Based on Feature Extraction of Laser Scanner Using Self-organizing Feature Mapping. Proceedings of the 4th International Symposium on Neural Networks: Advances in Neural Networks,Nanjing, China, 2007, 4491: 743-748
62 S.I. Roumeliotis, G.A. Bekey. SEGMENTS: A layered, Dual-Kalman Filter Algorithm for Indoor Feature Extraction. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2000: 454-461
63黄明登,肖晓明,蔡自兴,等.机器人局部环境特征提取方法的研究.计算机测量与控制, 2007, 15(2): 241-244
64 A. Scott, L.E. Parker and C. Touzet. Quantitative and Qualitative Comparison of Three Laser-range Mapping Algorithms Using Two Types of Laser Scanner Data. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2000, 2: 1422-1427
65 J. Gonzalez, A. Ollero and A. Reina. Map Building for Mobile Robot Equipped with a
2D Laser Rangefinder. Proceedings of the IEEE International Conference on Robotics and Automation. 1994, 3: 1904-1909
66 B.D. Yim, Y.J. Lee, J.B. Song and W. Chung. Mobile Robot Localization Using Fusion of Object Recognition and Range Information. Proceedings of the IEEE International Conference on Robotics and Automation. Roma, Italy. April, 2007: 3533-3538
67 A. Walthelm. New Approach to Global Self-localization with Laser Range Scans in Unstructured Environments. Proceedings of the IEEE Intelligent Vehicle Symposium, 2002: 17-21
68 J. Vandorpe, H.B. Van and H. Xu. Exact Dynamic Map Building for a Mobile Robot Using Geometrical Primitives Produced by a 2D Range Finder. Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota. April, 1996: 901-908
69 G.S. Lionis and K.J. Kyriakopoulos. A Laser Scanner Based Mobile Robust SLAM Algorithm with Improved Convergence Properties. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Lausanne, Switzerland. October, 2002: 582-587
70 L. Zhang, B.K. Ghosh. Line Segment Based Map Building and Localization Uing 2D Laser Rangefinder. Proceedings of the IEEE International Conference on Robotics andAutomation, 2000, 3: 2538-2543
71 J. Forsberg, U. Larsson and A. Wernersson. Mobile Robot Navigation Using the Range-weighted Hough Transform. Robotics & Automation, 1995, 2(1): 18-26
72 K.O. Arras, R.Y. Siegwart. Feature Extraction and Scene Interpretation for Map-based Navigation and Map Building. Proceedings of SPIE, Mobile Robotics XII. 1997: 42-53
73 P. Newman, J. Leonard, J.D. Tardos, et al. Explore and Return: Experimental Validation of Real-time Concurrent Mapping and Localization. Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC. May, 2002, 2: 1802-1809
74 R. Madhavan, H. Durrant-Whyte and G. Dissanayake. Natural Landmark-based Autonomous Navigation Using Curvature Scale Space. Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC. May, 2002, 4: 3936-3941
75 R.H. Byrne, P.R. Klarer and J.B. Pletta. Techniques for Autonomous Navigation. In Sandia Report SAND92-0457. Sandia National Laboratories, Albuquerque, NM. March, 1992
76 G. Weiss, C. Wetzler and E.V. Puttkamer. Keeping Track of Position and Orientation of Moving Indoor Systems by Correlation of Range-finder Scans. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, 1994: 595-601
77 J.S. Gutmann, T. Weigel and B. Nebel. Fast, Accurate, and Robust Self-localization in Polygonal Environments. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 1999: 1412-1419
78 F. Lu, E.E. Milios. Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans. Journal of Intelligent and Robotic Systems, 1997, 18(3): 249-275
79 I.J. Cox. Blanche-An Experiment in Guidance and Navigation of an Autonomous Robot Vehicle. Transactions on Robotics and Automation, 1991, 7(2): 193-204
80 J.S. Gutmann, C. Schlegel. AMOS: Comparison of Scan Matching Approaches for Self-localization in Indoor Environments. Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots, 1996: 61-67
81 J. Weber, L. Franken, K.L. Jorg and E. Puttkamer. Reference Scan Matching for Global Self-localization. Robotics and Autonomous Systems, 2002, 40: 99-110
82 J.S. Gutmann, D. Fox. An experimental comparison of localization methods continued. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and System, 2002, 1: 454-459
83 P. Jensfelt, H.I. Christensen. Pose Tracking Using Laser Scanning and Minimalist Environment Models. Proceedings of the IEEE Transactions on Robotic and Automation, 2001, 17(2): 138-147
84 K.O. Arras, J.A. Castellanos, M. Schilt and R. Siegwart. Feature-based Multi-hypothesis Localization and Tracking Using Geometric Constraints. Robotics and Autonomous Systems (Elsevier), 2003, 44: 41-53
85 M. Altermatt, A. Martinelli, N. Tomatis and R. Siegwart. SLAM with Corner Features Based on a Relative Map. Proceedings of the International Conference on Intelligent Robots and Systems, Sendai, Japan, September, 2004, 2: 1053-1058
86 Z.Z. Xu, R.H. Liang and J.L. Liu. Global Localization Based on Corner Point. Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, July, 2003, 2: 843-847
87项志宇,刘济林.室内移动机器人的初始定位的一种新方法.仪器仪表学报, 2003, 24(1): 65-68
88王洪波,田行斌,张海明,等.差动机构在全方位移动机器人上的应用.机械设计与研究, 2008, 2: 25-26
89 Qijing Tang, Zhengwei Hu, Zhengyan Qi, Ke Yu, Xingbin Tian and Hongbo Wang. Kinematics Analysis and Simulation of a 2-DOF Omni-directional Mobile Robot. Mechine Design and Research, 2008, 24: 260-263
2郑向阳,熊蓉,顾大强.移动机器人导航和定位技术.机电工程, 2003, 20(5): 35-37
3李磊,叶涛,谭民,等.移动机器人技术研究现状与未来.机器人, 2002, 24(5): 475-480
4徐国华,谭民.移动机器人的发展现状及其趋势.机器人技术与应用, 2001, 3: 7-13
5朱淼良,杨建刚,吴春明.自主式智能系统.浙江:浙江大学出版社, 2000: 1-155
6 G.N. Saridis. Toward the Realization of Intelligent Controls. Proceedings of the IEEE, 1979, 67(8): 1115-1133
7 R. Brooks. A Robust Layered Control System for a Mobile Robot. Journal of Robotics and Automation, 1986, 2(l): 14-23.
8 H.F. Durrant-Whyte. Where am I? A Tutorial on Mobile Vehicle Localization. Industrial Robot, 1994, 21(2): 11-16
9王挺,王越超.人机系统在移动机器人平台的应用.机器人, 2004, 26(6): 553-557
10王立娜.视觉临场感遥控系统在移动机器人中的应用.中国科技信息, 2007, 19: 265-266
11 G.A. Borges, M.J. Aldon. Optimal Mobile Robot Pose Estimation Using Geometrical Maps. Proceedings of the IEEE Transaction on Robotics and Automation, 2002, 1: 87-93
12 S.Y. Kim, S.H. Park. Estimation of Absolute Positioning of Mobile Robot Using U-STA. Lecture Notes in Computer Science, 2008, 4672: 143-150
13 L. Moreno, et al. A Genetic Algorithm for Mobile Robot Localization Using Ultrasonic Sensors. Journal of Intelligence and Robotics Systems, 2002, 34: 135-154
14杨明,王宏,张钹.基于激光雷达的移动机器人位姿估计方法综述.机器人, 2002, 24(2): 177-183
15 W. Burgard, et al. Estimation the Absolute Position of a Mobile Robot Using Position Probability Grids. Proceedings of the National Conference on Artificial Intelligence. Portland, Oregon, USA, 1996: 896-901
16张毅,罗元,郑太雄,等.移动机器人技术及其应用.北京:电子工业出版社, 2007: 139-165
17 D. Fox, W. Burgard, F. Dellaert, et al. Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. Proceedings of the National Conference on Artificial Intelligence, 1999: 343-349
18 S. Thrun, D. Fox, W. Burgard, et al. Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 2002, 128: 99-141
19 C. Gonzalez-Buesa, J. Campos. Solving the Mobile Robot Localization Problem Using String Matching Algorithms. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, 3(28): 2475-2480
20孟庆杰,董绪荣. GPS/DR组合导航系统故障探测与剔除(FDI)的容错设计.装备指挥技术学院学报, 2002, 13(3): 40-43
21郭圣权,李杰.车辆兵器GPS/DR/DM组合导航系统的研究.火力与指挥控制, 2002, 27(5): 13-16
22 G. Jang, S. Kim, W. Lee and I. Kweon. Robust Self-localization of Mobile Robots Using Artificial and Natural Landmarks. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, 2003, 1: 412-417
23孟凯,孙茂相,孙金根.基于主动路标的全方位移动机器人定位系统.沈阳工业大学学报, 2000, 22(2): 148-151
24 O. Wijk, H.I. Christensen. Localization and Navigation of a Mobile Robot Using Natural Point Landmarks Extracted from Data. Robotics and Autonomous Systems, 2000, 3: 131-142
25唐琏,白涛,蔡自兴.移动机器人的一种室内自然路标定位法.计算机工程与应用, 2005, 4(15): 44-47
26 V.T. Nguyen, M.S. Jeong, S.M. Ahn, et al. A Robust Localization Method for MobileRobots Based on Ceiling Landmarks. Lecture Notes in Computer Science, 2007, 4617: 422-430
27 Hongbo Wang, Lingfu Kong and Hongnian Yu. Ceiling Light Landmarks Based Localization and Motion Control for a Mobile Robot. Proceedings of the IEEE International Conference on Networking, Sensing and Control. London, April 15-17, 2007: 285-290.
28 R. Simmon, S. Koening. Probabilistic Navigation in Partially Observable Environments. Proceedings of the International Joint Conference on Artificial Intelligence, 1995: 1080-1087
29 Xu Zezhong, Liu Jilin and Xiang Zhiyu. Scan Matching Based on CLS Relationships. Proceedings of IEEE International Conference on Robotics, Intelligent Systems and Signal Processings, Changsha, China, 2003: 99-104.
30王志文,郭戈.移动机器人导航技术现状与展望.机器人, 2003, 25(5): 470-474
31 J.L. Blanco, J.A. Fernández-Madrigal and J. Gonzalez. A Novel Measure of Uncertainty for Mobile Robot SLAM with Rao-Blackwellized Particle Filters. The International Journal of Robotics Research, 2008, 27(1): 73-89
32 D.F. Wolf, G.S. Sukhatme. Online Simultaneous Localization and Mapping in Dynamic Environments. Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, 2004: 1301-1306
33 D.F. Wolf, G.S. Sukhatme. Mobile Robot Simultaneous Localization and Mapping in Dynamic Environment. Autonomous Robots, 2005, 19: 53-65
34 D. H?hnel, R. Triebel, W. Burgard, et al. Map Building with Mobile Robots in Dynamic Environment. Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, 2003: 1557-1563
35 G.Q. Huang, A.B. Rad and Y.K. Wong. A New Solution to Map Dynamic Indoor Environments. International Journal of Advanced Robotic System, 2006, 3(2): 199-210
36 C. Stachniss, W. Burgard. Mobile Robot Mapping and Localization in Non-static Environments. Proceeding of the International Conference on Artificial Intelligence.Pensylvania, 2005: 1324-1329
37 M. Montemerlo, S. Thrun. Fast SLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. Proceedings of the 18th National Conference on Artificial Intelligence, 2002: 593-598
38 J.A. Byrne. 21 Ideas for the 2lst Century. Business Week, Aug.30, 1999: 78-167
39 Yuqing Gao, Ron Weiss, Peidong Yang, et al. 10 Emerging Technologies That Will Change Your World. Technology Review, February, 2004, 106(l): 32-50
40李文仲,段朝玉. ZigBee2006无线网络与无线定位实战.北京:北京航空航天大学出版社, 2008: 1-382
41 P. Baronti, P. Pillai, V. Chook, et al. Wireless Sensor Networks: A Survey on the State of the Art and the 802.15.4 and ZigBee Standards. Computer Communications, 2007: 1655-1695
42李响,王川,刘序文,等.基于ZigBee的无线传感器网络定位系统的研究.中国科技论文在线, http://www.paper.edu.cn
43段渭军,王建刚,王福豹.无线传感器网络节点定位系统与算法的研究和发展.信息与控制, 2006, 35(2): 239-245
44 T. He, C.D. Huang and B.M. Blum. Range-free Localization Schemes for Large Scale Sensor Networks. Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, San Diego, California, USA, 2003: 81-95
45 N. Bulusu, J. Heidemann and D. Estrin. GPS-less Low Cost Outdoor Localization for Very Small Devices. Personal Communications, 2000, 7(5): 28-34
46 D. Niculescu, B. Nath. DV-based Positioning in Ad Hoc Networks. Journal of Telecommunication Systems, 2003, 22(1/4): 267-280
47傅明磊,董慧颖.无线传感器网络节点定位技术研究.科技咨询导报, 2007, 3: 28-29
48 L. Doherty, K.S.J. Pister and L.E. Ghaoui. Convex Position Estimation in Wireless Sensor Networks. Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies(INFOCOM), Anchorage, AK, USA: 2001, 3: 1655-1663
49 Y. Shang, W. Ruml, Y. Zhang and M. Fromherz. Localization from Mere Connectivity. Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking & computing, Annapolis, Maryland, USA, 2003: 201-212
50章浩,李萍萍,张西良.无线传感器网络节点定位机制研究.宽带网络与传输, 2006, 10: 70-73
51周正.无线传感器网络的节点自定位技术.中兴通讯技术, 2005, 11(4): 51-56
52 Chipcon, http://www.chipcon.com/files/CC2431_Location_Engine. Pdf, 2006
53宋文,无线传感器网络技术与应用.北京:电子工业出版社, 2007: 105-106
54陈维克,李文锋,首珩,等.基于RSSI的无线传感器网络加权质心定位算法.武汉理工大学学报(交通科学与工程版), 2006, 30(2): 265-268
55 D. Taubenheim, S. Kyperountas and N. Correal. Distributed Radiolocation Hardware Core for IEEE 802.15.4. http://www.chipcon.com/files/Radioloeation_Engine_WP. Pdf, 2005
56 D. Kortenkamp, T. Weynouth. Topological Mapping for Mobile Robots Using a Combination of Sonar and Vision Sensing. Proceeding of the 12th National Conference on Artificial Intelligence, Menlo Park, 2004, 2: 979-984
57 S. Thrun, A. Bücken. Integrating Grid-based and Topological Maps for Mobile Robot Navigation. Proceedings of 13th National Conference on Artificial Intelligence, Portland Oergon, 1996, 2: 944-950
58李云翀,何克忠.基于激光雷达的室外移动机器人避障与导航新方法.机器人, 2006, 28(3): 275-278
59蔡自兴,郑敏捷,邹小兵.基于激光雷达的移动机器人实时避障策略.中南大学学报(自然科学版), 2006, 37(2): 324-329
60 Y. Cang, J. Borenstein. Characterization of a 2-D Laser Scanner for Mobile Robot Obstacle Navigation. Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC, 2002, 3: 2512-2518
61 J.X. Yu, Z.X. Cai and Z.H. Duan. Mobile Robot Self-localization Based on Feature Extraction of Laser Scanner Using Self-organizing Feature Mapping. Proceedings of the 4th International Symposium on Neural Networks: Advances in Neural Networks,Nanjing, China, 2007, 4491: 743-748
62 S.I. Roumeliotis, G.A. Bekey. SEGMENTS: A layered, Dual-Kalman Filter Algorithm for Indoor Feature Extraction. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2000: 454-461
63黄明登,肖晓明,蔡自兴,等.机器人局部环境特征提取方法的研究.计算机测量与控制, 2007, 15(2): 241-244
64 A. Scott, L.E. Parker and C. Touzet. Quantitative and Qualitative Comparison of Three Laser-range Mapping Algorithms Using Two Types of Laser Scanner Data. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2000, 2: 1422-1427
65 J. Gonzalez, A. Ollero and A. Reina. Map Building for Mobile Robot Equipped with a
2D Laser Rangefinder. Proceedings of the IEEE International Conference on Robotics and Automation. 1994, 3: 1904-1909
66 B.D. Yim, Y.J. Lee, J.B. Song and W. Chung. Mobile Robot Localization Using Fusion of Object Recognition and Range Information. Proceedings of the IEEE International Conference on Robotics and Automation. Roma, Italy. April, 2007: 3533-3538
67 A. Walthelm. New Approach to Global Self-localization with Laser Range Scans in Unstructured Environments. Proceedings of the IEEE Intelligent Vehicle Symposium, 2002: 17-21
68 J. Vandorpe, H.B. Van and H. Xu. Exact Dynamic Map Building for a Mobile Robot Using Geometrical Primitives Produced by a 2D Range Finder. Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota. April, 1996: 901-908
69 G.S. Lionis and K.J. Kyriakopoulos. A Laser Scanner Based Mobile Robust SLAM Algorithm with Improved Convergence Properties. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Lausanne, Switzerland. October, 2002: 582-587
70 L. Zhang, B.K. Ghosh. Line Segment Based Map Building and Localization Uing 2D Laser Rangefinder. Proceedings of the IEEE International Conference on Robotics andAutomation, 2000, 3: 2538-2543
71 J. Forsberg, U. Larsson and A. Wernersson. Mobile Robot Navigation Using the Range-weighted Hough Transform. Robotics & Automation, 1995, 2(1): 18-26
72 K.O. Arras, R.Y. Siegwart. Feature Extraction and Scene Interpretation for Map-based Navigation and Map Building. Proceedings of SPIE, Mobile Robotics XII. 1997: 42-53
73 P. Newman, J. Leonard, J.D. Tardos, et al. Explore and Return: Experimental Validation of Real-time Concurrent Mapping and Localization. Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC. May, 2002, 2: 1802-1809
74 R. Madhavan, H. Durrant-Whyte and G. Dissanayake. Natural Landmark-based Autonomous Navigation Using Curvature Scale Space. Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC. May, 2002, 4: 3936-3941
75 R.H. Byrne, P.R. Klarer and J.B. Pletta. Techniques for Autonomous Navigation. In Sandia Report SAND92-0457. Sandia National Laboratories, Albuquerque, NM. March, 1992
76 G. Weiss, C. Wetzler and E.V. Puttkamer. Keeping Track of Position and Orientation of Moving Indoor Systems by Correlation of Range-finder Scans. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, 1994: 595-601
77 J.S. Gutmann, T. Weigel and B. Nebel. Fast, Accurate, and Robust Self-localization in Polygonal Environments. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 1999: 1412-1419
78 F. Lu, E.E. Milios. Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans. Journal of Intelligent and Robotic Systems, 1997, 18(3): 249-275
79 I.J. Cox. Blanche-An Experiment in Guidance and Navigation of an Autonomous Robot Vehicle. Transactions on Robotics and Automation, 1991, 7(2): 193-204
80 J.S. Gutmann, C. Schlegel. AMOS: Comparison of Scan Matching Approaches for Self-localization in Indoor Environments. Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots, 1996: 61-67
81 J. Weber, L. Franken, K.L. Jorg and E. Puttkamer. Reference Scan Matching for Global Self-localization. Robotics and Autonomous Systems, 2002, 40: 99-110
82 J.S. Gutmann, D. Fox. An experimental comparison of localization methods continued. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and System, 2002, 1: 454-459
83 P. Jensfelt, H.I. Christensen. Pose Tracking Using Laser Scanning and Minimalist Environment Models. Proceedings of the IEEE Transactions on Robotic and Automation, 2001, 17(2): 138-147
84 K.O. Arras, J.A. Castellanos, M. Schilt and R. Siegwart. Feature-based Multi-hypothesis Localization and Tracking Using Geometric Constraints. Robotics and Autonomous Systems (Elsevier), 2003, 44: 41-53
85 M. Altermatt, A. Martinelli, N. Tomatis and R. Siegwart. SLAM with Corner Features Based on a Relative Map. Proceedings of the International Conference on Intelligent Robots and Systems, Sendai, Japan, September, 2004, 2: 1053-1058
86 Z.Z. Xu, R.H. Liang and J.L. Liu. Global Localization Based on Corner Point. Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, July, 2003, 2: 843-847
87项志宇,刘济林.室内移动机器人的初始定位的一种新方法.仪器仪表学报, 2003, 24(1): 65-68
88王洪波,田行斌,张海明,等.差动机构在全方位移动机器人上的应用.机械设计与研究, 2008, 2: 25-26
89 Qijing Tang, Zhengwei Hu, Zhengyan Qi, Ke Yu, Xingbin Tian and Hongbo Wang. Kinematics Analysis and Simulation of a 2-DOF Omni-directional Mobile Robot. Mechine Design and Research, 2008, 24: 260-263