Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19834
標題: 改善〝EKF-SLAM〞之準確性與效率
Improving the accuracy and efficiency of EKF-SLAM
作者: 余俊瑩
Yu, Jiun-Ying
關鍵字: 行動型機器人
運動模型
觀測模型
地圖
同時定位與建立環境地圖
貝氏濾波器
卡門濾波器
延伸卡門濾波器
出版社: 資訊科學與工程學系所
引用: [1]林惠玲、陳正倉合著。統計學 方法與應用(四版)。 [2]康仕仲。智慧型機器人 程式開發與實作。 [3]謝祥文、張彥中、蔡雨坤、鄭明育。「慣性視覺感測器融合之同步定位與地圖建置技術」 機械工業雜誌329期 2010。頁12-20。 [4]楊劭文、王傑智。「行動型機器人定位的挑戰」。智慧型機器人產業情報報告第32期。頁10~27。 [5]張峻華、楊劭文、王傑智。「行動型機器人的環境感知」。機器人產業情報報告第34期。頁10-21。 [6]Sebastian Thrun, Wolfram Burugard, and Dieter Fox,eds., Probabilistic Robotics. London: MIT Press, 2006. [7]Stuart Russell and Peter Norvig , Artificial Intelligence: A Modern Approach ,3rd ed. Prentice Hall, 2009. [8]HyungSoo Lim, ByoungSuk Choi and JangMyung Lee, “An Efficient Localization Algorithm for Mobile Robots based on RFID System.” in SICE-ICASE, 2006 . [9]Sunhong Park, Saegusa R. and Hashimoto S., “Autonomous navigation of a mobile robot based on passive RFID.”in Robot and Human interactive Communication, 2007. [10]Milella A., Di Paola D., Cicirelli G. and D''Orazio T., “RFID tag bearing estimation for mobile robot localization.”in Advanced Robotics, 2009. [11]SeungKeun Cho, TaeKyung Yang, MunGyu Choi and JangMyung Lee, “ Localization of a high-speed mobile robot using global features.”in Autonomous Robots and Agents, 2009. [12]Takemura, K.,Araki, A., Ido, J., Matsumoto, Y., Takamatsu, J. and Ogasawara T.,“Generating individual maps from Universal map for heterogeneous mobile robots”in Robotics and Automation (ICRA), 2010 IEEE International . [13]Della Vedova, M.L.,Facchinetti, T., Ferrara, A.and Martinelli A.,“Visual Interaction for Real-Time Navigation of Autonomous Mobile Robots ”in CyberWorlds, 2009 International Conference. [14]Davison, A.J., Reid, I.D.; Molton, N.D.; Stasse, O.; MonoSLAM: Real-Time Single Camera SLAM Pattern Analysis and Machine Intelligence, IEEE Transactions on 2007 page1052-1067 [15]Seo-Yeon Hwang and Jae-Bok Song, “Monocular Vision-Based SLAM in Indoor Environment Using Corner, Lamp, and Door Features From Upward-Looking Camera”The IEEE Transactions 58:10(October 2011):4804 - 4812 . [16]M. Montemerlo, S. Thrun, D. Koller and B. Wegbreit., “FastSLAM: A factored solution to the simultaneous localization and mapping problem.” in Proceedings of the AAAI National Conference on Artificial Intelligence, Edmonton, Canada, 2002. AAAI. [17]M. Montemerlo, S. Thrun, D. Koller and B. Wegbreit. “FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. ”in Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico, 2003. IJCAI. [18]Calonder M., “EKF SLAM vs. FastSLAM: A Comparison.” Technical Report CVLAB-REPORT-2010-001. [19]Zang Z.,“A Flexible New Technique for Camera Calibration ”The IEEE Transactions 22:11(November 2011):1330 - 1334 . [20] Jianbo Shi, Tomasi C.,“Good Features to Track”Computer Vision and Pattern Recognition, 1994. Proceedings CVPR ''94., 1994 IEEE Computer Society. [21] R.E. Kalman,“A new approach to linear filtering and prediction problems”Journal of Basic Engineering, No. 82 (Series D). (1960): 35-45. [22]Durrant-Whyte H.and Bailey T.,“Simultaneous Localization and Mapping : Part I.”Robotics & Automation Magazine,IEEE 13:2 (June 2011):99-110. [23] SLAM SUMMER SCHOOL,2009, http://www.acfr.edu.au/education/summerschool.shtml [24]wiki Isaac Asimov http://en.wikipedia.org/wiki/Isaac_Asimov [25]wiki Three Laws of Robotics http://en.wikipedia.org/wiki/Three_Laws_of_Robotics [26]wiki ASIMO http://world.honda.com/ASIMO/ [27]Bill Gates,Scientieic American,February 2007. http://sa.ylib.com/read/readshow.asp?FDocNo=966 [28] Ecci Robot Features Bones, Ligaments And Muscles. Can Learn From His Mistakes.http://0rz.tw/pe4bQ [29]iRobot. http://store.irobot.com/home/index.jsp [30]NAO. http://www.aldebaran-robotics.com/ [31]LEGO NXT. http://mindstorms.lego.com/en-us/Default.aspx [32]Robosapiens. http://www.wowwee.com/en/products/toys/robots/robotics/ [33]AGAiT. http://www.agaitech.com/homepage.jsp [34]Taipei MRT. http://www.trtc.com.tw/MP_122031.html
摘要: 機器人應用大略分工業性與服務性,如工業用機械手臂或者目前當紅的娛樂型機器人,為了能深入人類生活並且完成自主任務,以「智慧行動型機器人」為發展主流。 大部分行動型機器人都會面臨解決定位問題和描述環境地圖兩個大議題,目前以SLAM能同時解決兩大議題是相當熱門的研究方向,本文提出一種基於傳統EKF-SLAM方法,使用三角定位與臨界值法來增進行動型機器人定位的準確性與效率。
The applications of robots are roughly divided into industrial-oriented, such as robot arms, and service-oriented, such as entertainment robots. In order to complete autonomous tasks and integrate into human life , the“ smart mobile robots” are the mainstream in robot development. In most mobile robot systems, localization and map modelling are two important issues. They are the so-called SLAM (simultaneous localization and mapping) problem which is a very popular research topic. In this thesis we propose a new approach that use triangulation localization and threshold method for improving the accuracy and efficiency of EKF-SLAM.
URI: http://hdl.handle.net/11455/19834
Appears in Collections:資訊科學與工程學系所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.