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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
dc.contributor.author | Ming-Fang Chang | en |
dc.contributor.author | 張明芳 | zh_TW |
dc.date.accessioned | 2021-06-16T13:25:12Z | - |
dc.date.available | 2018-08-20 | |
dc.date.copyright | 2013-08-20 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-07-23 | |
dc.identifier.citation | [1] K. Lai, B. Liefeng, R. Xiaofeng, and D. Fox, 'Sparse distance learning for object recognition combining RGB and depth information,' in Robotics and Automation (ICRA), 2011 IEEE International Conference on, 2011, pp. 4007-4013.
[2] R. B. Rusu, G. Bradski, R. Thibaux, and J. Hsu, 'Fast 3D recognition and pose using the Viewpoint Feature Histogram,' in Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, 2010, pp. 2155-2162. [3] A. K. Mishra, A. Shrivastava, and Y. Aloimonos, 'Segmenting 'Simple' Objects Using RGB-D,' presented at the Robotics and Automation (ICRA), 2012 IEEE International Conference on, 2012. [4] J. G. Rogers, A. J. B. Trevor, C. Nieto-Granda, and H. I. Christensen, 'Simultaneous localization and mapping with learned object recognition and semantic data association,' in Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, 2011, pp. 1264-1270. [5] N. Silberman and R. Fergus, 'Indoor scene segmentation using a structured light sensor,' in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, 2011, pp. 601-608. [6] R. B. Rusu, N. Blodow, Z. Marton, A. Soos, and M. Beetz, 'Towards 3D object maps for autonomous household robots,' in Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, 2007, pp. 3191-3198. [7] S. Olufs and M. Vincze, 'Towards robust room structure segmentation in Manhattan-like environments from dense 2.5D data,' in Control, Automation and Systems (ICCAS), 2011 11th International Conference on, 2011, pp. 1491-1496. [8] H. S. Koppula, A. Anand, T. Joachims, and A. Saxena, 'Semantic Labeling of 3D Point Clouds for Indoor Scenes,' in Advances in Neural Information Processing Systems 24, ed, 2011, pp. 244-252. [9] A. Pronobis and P. Jensfelt, 'Large-scale Semantic Mapping and Reasoning with Heterogeneous Modalities,' presented at the Robotics and Automation (ICRA), 2011 IEEE International Conference on, 2012. [10] K. M. Wurm, D. Hennes, D. Holz, R. B. Rusu, C. Stachniss, K. Konolige, and W. Burgard, 'Hierarchies of octrees for efficient 3D mapping,' in Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, 2011, pp. 4249-4255. [11] P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, 'RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments,' The International Journal of Robotics Research, vol. 31, pp. 647-663, April 1, 2012 2012. [12] C. J. Taylor and A. Cowley, 'Fast scene analysis using image and range data,' in Robotics and Automation (ICRA), 2011 IEEE International Conference on, 2011, pp. 3562-3567. [13] K. Pathak, A. Birk, N. Vaskevicius, M. Pfingsthorn, S. Schwertfeger, and J. Poppinga, 'Online three-dimensional SLAM by registration of large planar surface segments and closed-form pose-graph relaxation,' J. Field Robot., vol. 27, pp. 52-84, 2010. [14] K. Pathak, N. Vaskevicius, and A. Birk, 'Revisiting uncertainty analysis for optimum planes extracted from 3D range sensor point-clouds,' in Robotics and Automation, 2009. ICRA '09. IEEE International Conference on, 2009, pp. 1631-1636. [15] N. Engelharda, F. Endresa, J. u. Hessa, J. u. Sturmb, and W. Burgarda, 'Real-time 3D visual SLAM with a hand-held RGB-D camera,' in RGB-D Workshop on 3D Perception in Robotics European Robotics Forum, 2011. [16] S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, A. Davison, and A. Fitzgibbon, 'KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera,' in ACM Symposium on User Interface Software and Technology, 2011. [17] K. Pathak, A. Birk, Vas, x030C, kevic, N. ius, and J. Poppinga, 'Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping,' Robotics, IEEE Transactions on, vol. 26, pp. 424-441, 2010. [18] I. Dryanovski, C. Jaramillo, and J. Xiao, 'Incremental Registration of RGB-D Images,' presented at the obotics and Automation (ICRA), 2012 IEEE International Conference on, 2012. [19] L. Chi-Pang, C. Chen-Tun, C. Kuo-Hung, and F. Li-Chen, 'Human-Centered Robot Navigation--Towards a Harmoniously Human-Robot Coexisting Environment,' Robotics, IEEE Transactions on, vol. 27, pp. 99-112, 2011. [20] C. Chen Tun, L. Jiun-Yi, C. Ming-Fang, and F. Li Chen, 'Multi-robot cooperation based human tracking system using Laser Range Finder,' in Robotics and Automation (ICRA), 2011 IEEE International Conference on, 2011, pp. 532-537. [21] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics: The MIT Press, 2005. [22] J. Poppinga, N. Vaskevicius, A. Birk, and K. Pathak, 'Fast plane detection and polygonalization in noisy 3D range images,' in Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, 2008, pp. 3378-3383. [23] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, 'SURF: Speeded Up Robust Features,' Computer Vision and Image Understanding (CVIU), vol. 110, pp. 346--359, 2008. [24] J. Elseberg, D. Borrmann, and A. Nuchter, 'Efficient processing of large 3D point clouds,' in Information, Communication and Automation Technologies (ICAT), 2011 XXIII International Symposium on, 2011, pp. 1-7. [25] S. Hinterstoisser, C. Cagniart, S. Ilic, P. Sturm, N. Navab, P. Fua, and V. Lepetit, 'Gradient Response Maps for Real-Time Detection of Textureless Objects,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 34, pp. 876-888, 2012. [26] J. W. Durham and F. Bullo, 'Smooth Nearness-Diagram Navigation,' in Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, 2008, pp. 690-695. [27] D. Herrera C, J. Kannala, and J. Heikkila, 'Joint Depth and Color Camera Calibration with Distortion Correction,' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PP, pp. 1-1, 2012. [28] J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, 'A Benchmark for the Evaluation of RGB-D SLAM Systems,' in Proc. of the International Conference on Intelligent Robot Systems (IROS), 2012. [29] F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers, and W. Burgard, 'An Evaluation of the RGB-D SLAM System,' in Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), St. Paul, MA, USA, 2012. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62052 | - |
dc.description.abstract | 為了讓機器人能在日常環境中執行各種任務,我們需要提高機器人對環境的理解能力。在本論文中,我們提出使用彩色與深度相機(RGB-D camera)建立語意地圖的演算法,在建立地圖的同時進行物體辨識,並建立物體之間的關係。藉由讓機器人認識環境中的各種物體,機器人可以執行更多高階行為,這些知識也能讓機器人聽懂人類的語音命令進行更自然的人機互動。另外,藉由整合不同種類的特徵資訊,機器人可以在多種環境中建立地圖、導航,及執行任務,不會受限於環境的種類。
在我們的系統中,藉由整合彩色與深度相機及雷射測距儀的資訊,機器人可以進行門口辨識、在擁擠的房間中計算出可移動的區域,並根據簡單的語意命令來執行尋物並取物的任務。 | zh_TW |
dc.description.abstract | Semantic mapping has become a popular and essential research topic for intelligent robots nowadays. In this thesis, we present a semantic mapping framework using heterogeneous features, and its realization in office environment. Instead of considering only dataset-based or dataset-free semantic information like previous researches, our system incorporates heterogeneous features so that it is applicable to different environments. Besides its flexibility, object recognition is performed during the online mapping process and relations representing the inter-relationships among objects in the environment are labeled and updated through each new measurement. In addition, by defining semantic commands, our robot can understand semantic meaning and perform high-level tasks.
Finally, a mechanism of object finding task is proposed in office environment. By combining the information from Laser Range Finder and RGB-D camera, our robot can detect doors and navigate in crowded environment, which are quite typical in an office building. To validate our framework, several experiments are conducted, and it shows that our system can indeed build the semantic map and carry out some high-level tasks in office environment successfully. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T13:25:12Z (GMT). No. of bitstreams: 1 ntu-102-R99921007-1.pdf: 3029034 bytes, checksum: 82c64f859eef05384d4a17c663b69d56 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員會審定書 i
中文摘要 iiv ABSTRACT v CONTENTS vi LIST OF FIGURES ix LIST OF TABLE xiv Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Challenges 2 1.3 Related Works 5 1.4 Objectives 8 1.5 Contributions 8 1.6 Thesis Organization 9 Chapter 2 Preliminary 11 2.1 System Overview 11 2.1.1 Semantic Mapping Using Heterogeneous Features-based Registration 12 2.1.2 Building Relations among Semantic Objects 12 2.2 Homogeneous Transformation in 3D Space 12 2.3 Covariance Intersection 14 2.4 Extended Kalman Filter 15 2.5 Least Square Solution for Non-full Rank Matrix 16 Chapter 3 Semantic Mapping with Heterogeneous Features-based Registration 18 3.1 Online Mapping Problem 21 3.2 Heterogeneous Feature Extraction 22 3.2.1 Registration Using Plane Patches 23 3.2.2 Registration Using SURF Feature Points 30 3.2.3 Desk Side Line Matching 33 3.2.4 Object Recognition 35 3.3 Building Relations among Semantic Objects 37 Chapter 4 Preforming Object Finding Task in Office Environment 42 4.1 Semantic Command 43 4.2 Robot Navigation 44 4.2.1 Finding Navigable Area by Fusing LRF and RGB-D Data 44 4.3 Grasping the target 47 Chapter 5 Experimental Results 49 5.1 Experimental Settings and Environment 49 5.1.1 Hardware 49 5.2 Registration Mechanism Evaluation 53 5.2.1 Environment with Insufficient Feature Points 53 5.2.2 General Environment with Hybrid Features 58 5.3 Building Relations among Semantic Objects 61 5.3.1 Object Recognition Using Template Matching 61 5.3.2 Building Relations among Semantic Objects 62 Chapter 6 Conclusion 65 Reference 66 | |
dc.language.iso | en | |
dc.title | 應用於辦公室機器人服務之基於異質特徵的語意地圖建置 | zh_TW |
dc.title | Building Semantic Map in Office Environment using Heterogeneous Feature-based Registration for Robotic Service | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 羅仁權(Ren C. Luo),陳祝嵩(Chu-Song Chen),黃漢邦(Han-Pang Huang),黃正民(Cheng-Ming Huang) | |
dc.subject.keyword | 語意地圖,異質特徵,彩色與深度攝影機, | zh_TW |
dc.subject.keyword | Semantic mapping,RGB-D camera,robotic service, | en |
dc.relation.page | 70 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-07-24 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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