跳到主要內容

臺灣博碩士論文加值系統

(3.236.84.188) 您好!臺灣時間:2021/08/06 10:36
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:郭柏瑋
研究生(外文):Kuo, Bor-Woei
論文名稱:適用於室內環境之快速機器人同步定位與建圖
論文名稱(外文):A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments using Line Segment Map
指導教授:黃錫瑜黃錫瑜引用關係
指導教授(外文):Huang, Shi-Yu
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:44
中文關鍵詞:&&&&&&&&
外文關鍵詞:Lightweight SLAMRao-BlackWellized particle filter (RBPF)Line segment mapIndoor environments
相關次數:
  • 被引用被引用:1
  • 點閱點閱:401
  • 評分評分:
  • 下載下載:64
  • 收藏至我的研究室書目清單書目收藏:0
現今機器人自動導航領域中,最重要的議題不外乎是讓機器人能夠在未探索過的環境中自我定位與建立環境地圖 (Simultaneous Localization and Mapping)。但由於SLAM為高複雜度的演算法,因此往往需要很長的運算時間與大量的記憶體空間,才能達到高準確度的自我定位與建立地圖。這篇論文中,我們提出了一個適用於室內環境之輕量化粒子濾波器 (Rao-Blackwellized Particle Filter) 來進行機器人定位,並藉由從雷射測距儀掃描環境後得到的距離資訊中,提取出線段 (line segment) 來描繪環境地圖,同時只存放這些線段在我們的地圖中來達到降低記憶體使用量的效果。由於一般室內環境中,主要的環境結構 (牆壁、門、家具…) 擁有互相垂直與平行的特性。因此在我們的系統中,只考慮具有正交特性的線段來保證建立地圖的準確度與降低機器人定位所需的運算量。其中我們是利用一個參考角度 (reference direction) 來判斷從雷射測距儀所截取出的線段中屬於環境的水平與垂直線段。並且藉由動態的修正參考角度,我們的演算法可在機器人的起始位置沒有與環境的主要架構對齊時,也可以順利的判斷出環境中的水平與垂直線段。
實驗結果顯示,我們所提出的方法不但可在複雜的室內環境中準確的將機器人定位與建立環境地圖,且當機器人行走一段距離並繞行環境一周後,能夠順利將地圖銜接起來 (loop closing)。同時,經由統計結果可見,相對於一般搭配柵格狀地圖之粒子濾波器的方法,我們所提出的輕量化粒子濾波器只需要極低的記憶體使用量與運算時間就可以達到準確的效果。

Simultaneous Localization and Mapping (SLAM) is an important technique for robotic system navigation. Due to the high complexity of the algorithm, SLAM usually needs long computational time or large amount of memory to achieve accurate results. In this paper, we present a lightweight Rao-Blackwellized Particle Filter (RBPF) based SLAM algorithm for indoor environments, which uses line segments extracted from the laser range finder as the fundamental map structure so as to reduce the memory usage. Since most major structures of indoor environments are usually orthogonal to each other, we can also efficiently increase the accuracy and reduce the complexity of our algorithm by exploiting this orthogonal property of line segments; that is, we treat line segments that are parallel or perpendicular to each other in a special way when calculating the importance weight of each particle. In our work, the orthogonal scan lines extracted from the sensor can be identified by a reference direction, which is modified each time the local map is built. By dynamically modifying the reference direction, our algorithm can successfully detect the orthogonal lines even when the robots initial pose is not aligned with the major structures of the environment.
Experimental Results shows that our work not only is capable of drawing maps in complex indoor environments but also can accurately closes a loop after the robot has traveled a long distance. Results also shows that our proposed light-and fast SLAM only needs very low amount of memory and much less computational time as compared to other grid map based RBPF SLAM algorithms.

Abstract i
中文摘要 ii
誌謝 iii
Content iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Related Work and Background 2
1.2 Thesis Organization 4
Chapter 2 Preliminaries 5
2.1 Bayes Filter 5
2.2 Particle Filter 6
2.3 Simultaneous Localization and Mapping (SLAM) 7
2.3.1 Rao-Blackwellized Particle Filter (RBPF) 7
Chapter 3 Proposed Lightweight SLAM 13
3.1 System Overview 13
3.2 Particle Generation 15
3.3 Line Segment Extraction 16
3.3.1 Least Square Fitting 17
3.3.2 Enhanced Sequential Segmentation Algorithm 19
3.3.3 Rotate and Shift 22
3.4 Data Association 24
3.4.1 Local Map 25
3.4.2 Orientation 26
3.4.3 Vector Based Line Representation 26
3.4.4 Data Association 28
3.5 Importance Weighting 29
3.6 Map Update 31
3.6.1 Local Map Update 32
3.6.2 Global Map Update 33
Chapter 4 Experimental Results 34
4.1 System Configuration 34
4.2 Orthogonal Line Detection 35
4.3 Performance Comparison 37
Chapter 5 Conclusion 41
Bibliography 42
[1]W. Jeong and K. Lee, “CV-SLAM: A new ceiling vision-based SLAM technique,” In IEEE/RJS Int. Conf. on Intelligent Robots and Systems, 2005.
[2]H. Myung, H.-M. Jeon, and W.-Y. Jeong, “Virtual Door Algorithm for Coverage Path Planning of Mobile Robot,” IEEE International Symposium on Industrial Electronics (ISIE), 2009.
[3]V. Nguyen, A. Harati, A. Martinelli, and R. Seigwart, “Orthogonal SLAM: A step toward lightweight indoor autonomous navigation”, In Proceedings of IROS, 2006.
[4]K. Murphy. “Bayesian map learning in dynamic environments”, In Proc. of the Conf. on Neural Information Processing Systems (NIPS), pages 1015-1021, Denver, CO, USA, 1999.
[5]A. Doucet, J.F.G. de Freitas, K. Murphy, and S. Russel. “Rao-blackwellized particle filtering for dynamic bayesian networks”, Proc. of the Conf. on Uncertainty in Artificial Intelligence (UAI), 2000.
[6]G. Grisetti, C. Stachniss, and W. Burgard, “Improved techniques for grid mapping with Rao-Blackwellized particle filters”, IEEE Transactions on Robotics, vol. 23, pp. 34-46, Feb 2007.
[7]S. Thrun, W. Burgard, and D. Fox. “A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping,” In Proc. of the IEEE Interational Conference on Robotics and Automation (ICRA), 2000.
[8]Y.-H Choi, T.-K. Lee, and S.-Y. Oh, “A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot”, Autonomous Robots, vol. 24, pp. 13-27, 2008.
[9]P. Newman, J. Leonard, J. Tardos, and J. Neira, “Explore and Return: Experimental validation of real-time concurrent mapping and localization,” In Proc. of the IEEE Interational Conference on Robotics and Automation (ICRA), 2002.
[10]S. Thrun, W. Burgard, D. Fox, “Probabilistic Robotics”, The MIT Press, 2005.
[11]A. Eliazar and R. Parr. “Learning probabilistic motion models for mobile robots”, in International Conference on Machine Learning, 2004.
[12]H. J. Sohn, and B. K. Kim, “An efficient localization algorithm based on vector matching for mobile robots using laser range finders,” Journal of Intelligent and Robotic Systems, vol. 51, pp. 461-488, 2007.
[13]H. J. Sohn and B.K. Kim, “VecSLAM: An efficient vector-based SLAM algorithm for indoor environments”, Journal of Intelligent and Robotic Systems, vol. 56, pp. 301-318, 2009.
[14]F. Lu, and E. Milios, “Robot pose estimation in unknown environments by matching 2D range scans,” Journal of Intelligent and Robotic Systems, vol. 18, pp.249-275, 1997.
[15]G. A. Borges, and M. Aldon, “Line extraction in 2D range images for mobile robotics,” Journal of Intelligent and Robotic Systems, vol. 40, pp. 267-297, 2004.
[16]http://cres.usc.edu/radishrepository/view-all.php
[17]M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit. “FastSLAM: A factored solution to the simultaneous localization and mapping problem,” Proc. AAAI, 2002.
[18]A. Eliazar and R. Parr. “DP-SLAM: Fast, robust simultaneous localization and mapping without predetermined landmarks”, In Proc. Of the Int. Conf. on Artificial Intelligence (IJCAI), 2003.
[19]A. Eliazar and R. Parr. “DP-SLAM 2.0,” In Proc. ICRA, 2004.
[20]W.-J. Kuo, S.-H. Tseng, J.-Y. Yu, and L.-C. Fu, “A hybrid approach to RBPF based SLAM with grid mapping enhanced by line matching”, In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009.
[21]D. H¨ahnel, D. Schulz, and W. Burgard, “Mapping with mobile robots in populated environments,” In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009.
[22]C. Schroter, H.-J. Bohme, H.-M. Gross, “Memory-efficient gridmaps in Rao-Blackwellized particle filters for SLAM using sonar range sensors,” In Proc. 3rd ECMR, 2007
[23]F. Dellaert, D. Fox, W. Burgrad, and S. Thrun. “Monte Carlo localization for robots”, Proc. of International Conference on Robotics and Automation (ICRA), 1999.
[24]D. H¨ahnel, W. Burgard, D. Fox, and S. Thrun, “An efficient FastSLAM algorithm for generating maps of large-scale cyclic environment from raw laser range measurments”, In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2003
[25]http://www.cs.duke.edu/~parr/dpslam/

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top