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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Wei-Jen Kuo | en |
| dc.contributor.author | 郭維楨 | zh_TW |
| dc.date.accessioned | 2021-06-15T02:43:43Z | - |
| dc.date.available | 2011-08-17 | |
| dc.date.copyright | 2009-08-17 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-08-10 | |
| dc.identifier.citation | [1] R. Smith, M. Self, and P. Cheeseman, “Estimating uncertain spatial relationships in robotics,” Autonomous Robot Vehicles, I.J. Cox and G.T. Wilfon, Ed. , New York: Springer Verlag, pp. 167-193, 1990.
[2] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A factored solution to the simultaneous localization and mapping problem,” Proc. AAAI Nat’l Conf. Artificial Intelligence, 2002. [3] K. P. Murphy. Bayesian map learning in dynamic environments. In NIPS-12, pages 1015– 1021, 2000. [4] A. Eliazar and R. Parr, “DP-SLAM: Fast, robust simultaneous localization and mapping without predetermined landmarks,” in Proc. 18th Int. Joint Conf. on Artificial Intelligence (IJCAI-03), Morgan Kaufmann 2003, pp. 1135–1142. [5] A. Eliazar and R. Parr “DP-SLAM 2.0,” Proc. IEEE Int. Conf. Robot. Autom., vol. 2, p. 1314, May 2004. [6] G. Grisetti, C. Stachniss, and W. Burgard, “Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling,” in Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, 2005, pp. 2443–2448. [7] G. Grisetti , G. Tipaldi , C. Stachniss , W. Burgard and D. Nardi “Fast and accurate slam with Rao-Blackwellized particle filters,” Robot. Auton. Syst., vol. 55, pp. 30, 2007. [8] 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, 2007. [9] Zhang, L. and B. K. Ghosh. “Line segment based map building and localization using 2D laser rangefinder.” Proceedings. ICRA 2000. IEEE International Conference on Robotics and Automation, pp.2538-2543, 2000. [10] S. Pfister, S. Roumeliotis, and J. Burdick, “Weighted line fitting algorithms for mobile robot map building and efficient data representation,” in Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 1304–1311, Sep. 14-19 2003. [11] Nguyen, V. Harati, A. Martinelli, A. Siegwart, and R. Tomatis, N., 'Orthogonal SLAM: a Step toward Lightweight Indoor Autonomous Navigation,' 2006 IEEE/RSJ, International Conference on Intelligent Robots and Systems, pp.5007-5012, 9-15 Oct. 2006. [12] P. Nunez, R. Vazquez-Martin, J.C. del Toro, A. Bandera, and F. Sandoval, 'Feature extraction from laser scan data based on curvature estimation for mobile robotics,' Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp.1167-1172, 15-19 May 2006 [13] P. Nunez, R. Vazquez-Martin, A. Bandera, and F. Sandoval, 'CF-IDC: A robust robot’s self-localization in dynamic environments using curvature information,' Electrotechnical Conference, 2008. MELECON 2008. The 14th IEEE Mediterranean , pp.330-336, 5-7 May 2008 [14] A. I. Eliazar and R. Parr, “Learning probabilistic motion models for mobile robots” in International Conference on Machine Learning, p.32, 2004. [15] V. Nguyen, A. Martinelli, N. Tomatis, and R. Siegwart, “A Comparison of Line Extraction Algorithms using 2D Laser Rangefinder for Indoor Mobile Robotics” 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005), pp. 1929-1934, 2-6 Aug, 2005. [16] Joo Xavier, Marco Pacheco, Daniel Castro and Antnio Ruano “Fast line, arc/circle and leg detection from laser scan data in a Player driver” Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. ICRA 2005.pp. 3930-3935, 18-22 April 2005. [17] J. L. Blanco, J.A. Fernandez-Madrigal, and J.Gonz′alez, “Toward a unified Bayesian approach to hybrid metric-topological SLAM” IEEE Transactions on Robotics, vol.24, no.2, pp.259-270, April 2008. [18] A. Alempijevic, “High-speed feature extraction in sensor coordinates for laser rangefinders” in Proceedings of the 2004 Australasian Conference on Robotics and Automation, 2004. [19] G. 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. [20] R. Vazquez-Martin, P. Nunez, J.C. del Toro, A. Bandera, and F. Sandoval, 'Simultaneous mobile robot localization and mapping using an adaptive curvature-based environment description,' The 14th IEEE Mediterranean Electrotechnical Conference, 2008. MELECON 2008. pp.343-349, 5-7 May 2008. [21] Young-Ho Choi, Tae-Kyeong Lee, and Se-Young 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. [22] http://cres.usc.edu/radishrepository/view-all.php [23] M. Bosse, “ATLAS, A Framework for Large Scale Automated Mapping and Localization” Ph.D. dissertation, Massachusetts Institute of Technology, 2004. [24] Pedraza L., D. G., Valls Miró J., Rodriguez-Losada D., and Matía F. “BS-SLAM: Shaping the world.” In Proc. Robotics: Science and Systems, Atlanta, GA, USA, June 2007. [25] L. Pedraza, D. Rodriguez-Losada, F. Matia, G. Dissanayake, and J.V. Miro, “Extending the Limits of Feature-Based SLAM with B-Splines” IEEE Transactions on Robotics, , vol.25, no.2, pp.353-366, April 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44182 | - |
| dc.description.abstract | 這篇論文中，我們提出一種將環境表示為柵格狀地圖以及特徵點地圖混體的建圖結構，即FG (Feature-Grid)建圖。在室內環境中，以線段特徵點最為常見，他們提供了相當豐富的幾何資訊，因而可用於提高定位的精準度。線段的關係以互相傳值或平行最為常見，因此線段的正交特性是保證建圖結果一致性的要素之ㄧ。除此之外，角特徵點也提供了重要的定位資訊。這些特徵點使得粒子濾波器能夠更準確的推算機器人的位置。因此，這篇論文所提出的方法中，無須對環路型地圖執行額外的處理程序，便能確保環型路徑重疊時正確無誤。實驗結果顯示此方法在大型的室內環境也能順利運作，並成功地建立“環路”以及“無特徵點長廊”部分的地圖。做為實驗平台的機器人配備了最遠測量距離為20公尺的SICK LMS-100雷射測距儀。 | zh_TW |
| dc.description.abstract | In this thesis, we present a novel data structure representing the environment with occupancy grid cells while each grid map is associated with a set of features extracted from laser scan points, call FG (Feature-Grid) mapping. Due to the fact that line segments are principal elements of a great variety of indoor environments, they provide considerable geometric information about the environment and hence which can be used for enhancing the localization accuracy. Orthogonal characteristic of line features is the key to guarantee consistency of the resulting SLAM algorithm since the lines we are dealing with are either parallel or perpendicular to one another. Besides this, corners are also features that provide crucial location information. These special surrounding features allow the later used particle filter to sample robot poses more correctly. As a result, in this work the large loop map building can be closed without actually incorporating any loop closing mechanism. Experimental results are carried out successfully in relatively large challenging indoor environments, which contain both loops and long featureless corridors, with robots equipped with SICK LMS-100 laser scanner whose maximum range is 20 meters. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T02:43:43Z (GMT). No. of bitstreams: 1 ntu-98-R96922113-1.pdf: 852588 bytes, checksum: c5ee44c69bc4ec9c088f1a976891b4e4 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Preliminaries 8 2.1 Bayesian Filter 8 2.2 Particle Filter 8 2.2.1 Non-parametric Representation 9 2.2.2 Particle Filter Algorithm 9 2.3 RBPF (Rao-Blackwellized Particle Filter) Overview 10 2.4 DP-SLAM 11 2.4.1 Maintaining the Particle Ancestry Tree 11 2.4.2 Map Representation 11 Chapter 3 Hybrid Approach : FG-SLAM 13 3.1 Validity Analysis 13 3.2 Feature Enhanced RBPF Mapping 16 3.2.1 Sampling 16 3.2.2 Weighting 17 3.2.3 Resampling 18 3.2.4 Map estimation 18 3.3 Feature Extraction 20 3.3.1 Curvature Function 21 3.3.2 Line Extraction 25 3.3.3 Corner Extraction 27 3.4 Feature Association 28 3.4.1 Feature Association 29 3.4.2 Particle Weighting 33 3.5 Complexity 35 Chapter 4 Experimental Results 38 4.1 System Configuration 38 4.2 Particle Distribution Analysis 39 4.3 Loop Closing 40 4.4 Pose Variance Analysis 42 4.5 Handling Odometry Drift 43 4.6 Computation Time 44 Chapter 5 Conclusion 46 REFERENCE 47 PUBLICATION LIST 51 | |
| dc.language.iso | en | |
| dc.subject | 柵格特徵點混和式建圖 | zh_TW |
| dc.subject | 同步定位與建圖 | zh_TW |
| dc.subject | Rao-Blackwellized粒子濾波器 | zh_TW |
| dc.subject | 環路閉合 | zh_TW |
| dc.subject | 柵格狀建圖 | zh_TW |
| dc.subject | grid maps | en |
| dc.subject | feature-grid maps | en |
| dc.subject | Simultaneous localization & mapping (SLAM) | en |
| dc.subject | Rao-Blackwellized particle filters (RBPFs) | en |
| dc.subject | loop closure | en |
| dc.title | FG-SLAM: 幾合特徵點增強之混合式柵格狀定位建圖 | zh_TW |
| dc.title | FG-SLAM: A Hybrid Approach to Grid Based Mapping Enhanced by Geometric Features | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 簡忠漢(Jong-Hann Jean),李蔡彥(Tsai-Yen Li),周瑞仁(Jui-Jen Chou),施慶隆(Ching-Long Shih) | |
| dc.subject.keyword | 同步定位與建圖,Rao-Blackwellized粒子濾波器,環路閉合,柵格狀建圖,柵格特徵點混和式建圖, | zh_TW |
| dc.subject.keyword | Simultaneous localization & mapping (SLAM),Rao-Blackwellized particle filters (RBPFs),loop closure,grid maps,feature-grid maps, | en |
| dc.relation.page | 51 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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