請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林達德(Ta-Te Lin) | |
| dc.contributor.author | Rong-Siou Lee | en |
| dc.contributor.author | 李榕修 | zh_TW |
| dc.date.accessioned | 2021-06-15T16:10:55Z | - |
| dc.date.available | 2017-08-25 | |
| dc.date.copyright | 2015-08-25 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-08-18 | |
| dc.identifier.citation | 施志軒。2013。以SLAM機器人建立三維大尺度場景之演算法研究。碩士論文。臺北：國立臺灣大學生物產業機電工程學研究所。
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High quality 3D laser ranging under general vehicle motion. IEEE International Conference on Robotics and Automation. Hart, P. E., N. J. Nilsson and B. Raphael. 1968. A Formal Basis for the Heuristic Determination of Minimum Cost Paths. Systems Science and Cybernetics, IEEE Transactions on. 4(2), 100-107. Izadi, S., D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, A. Davison and A. Fitzgibbon. 2011. KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. Proceedings of the 24th annual ACM symposium on User interface software and technology, Santa Barbara, California, USA. Joung, J. H., K. H. An, J. W. Kang, M. J. Chung and W. Yu. 2009. 3D environment reconstruction using modified color ICP algorithm by fusion of a camera and a 3D laser range finder. IEEE International Conference on Intelligent Robots and Systems. Kazhdan, M., M. Bolitho and H. Hoppe. 2006. Poisson surface reconstruction. Proceedings of the fourth Eurographics symposium on Geometry processing, Cagliari, Sardinia, Italy. Korn, M., M. Holzkothen and J. Pauli. 2014. Color Supported Generalized-ICP. In Proceedings of VISAPP 2014 - International Conference on Computer Vision Theory and Applications, Lisbon, Portugal. Levenberg, K. 1994. A method for the solution of certain non-linear problems in least squares. Quarterly Journal of Applied Mathmatics, 164-168. Li, G., Y. Liu, L. Dong, X. Cai and D. Zhou. 2007. An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features. IEEE International Conference on Intelligent Robots and Systems. Lorensen, W. E. and H. E. Cline. 1987. Marching cubes: A high resolution 3D surface construction algorithm. SIGGRAPH Comput. Graph. 21(4), 163-169. Namoshe, M., O. Matsebe and N. Tlale. 2010. Corner Feature Extraction: Techniques for Landmark Based Navigation Systems, Sensor Fusion and its Applications Nuchter, A., H. Surmann, K. Lingemann, J. Hertzberg and S. Thrun. 2004. 6D SLAM with an application in autonomous mine mapping. IEEE International Conference on Robotics and Automation. Rusinkiewicz, S. and M. Levoy. 2001, 2001. Efficient variants of the ICP algorithm. 3-D Digital Imaging and Modeling, 2001. Proceedings. Third International Conference on. Russell, S. J., P. Norvig, J. F. Canny, J. M. Malik and D. D. Edwards. 1995. Artificial intelligence: a modern approach Rusu, R. B., Z. C. Marton, N. Blodow, M. Dolha and M. Beetz. 2008. Towards 3D Point cloud based object maps for household environments. Robotics and Autonomous Systems. 56(11), 927-941. Segal, A., D. Hähnel and S. Thrun. 2009. Generalized-ICP. Robotics: Science and Systems. Shih, J.-S. 2013. A Study on 3D Large-Scale Scenes Reconstruction Algorithms Using a SLAM Robot. Siegwart, R. and I. R. Nourbakhsh. 2004. Introduction to Autonomous Mobile Robots Smith, R. and C. Peter. 1986. On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research. 5((4)), 56-68. Sprickerhof, J., A. Nüchter, K. Lingemann and J. Hertzberg. 2009. An Explicit Loop Closing Technique for 6D SLAM. ECMR. Sprickerhof, J., A. Nüchter, K. Lingemann and J. Hertzberg. 2011. A Heuristic Loop Closing Technique for Large-Scale 6D SLAM. ATKAFF. 52(3), 199–222. Stentz, A. 1994. Optimal and efficient path planning for partially-known environments. IEEE International Conference on Robotics and Automation. Thrun, S., W. Burgard and D. Fox. 1998. A probabilistic approach to concurrent mapping and localization for mobile robots. Autonomous robots. 5(3-4), 253-271. Thrun, S., W. Burgard and D. Fox. 2005. Probabilistic robotics Wang, L. C., L. S. Yong and M. H. Ang Jr. 2002. Hybrid of global path planning and local navigation implemented on a mobile robot in indoor environment. IEEE International Symposium on Intelligent Control,. Wiemann, T., H. Annuth, K. Lingemann and J. Hertzberg. 2013, 25-29 Nov. 2013. An evaluation of open source surface reconstruction software for robotic applications. Advanced Robotics (ICAR), 2013 16th International Conference on. Wulf, O. and B. Wagner. 2003. Fast 3D scanning methods for laser measurement systems. In Proceedings of the International Conference on Control Systems. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52281 | - |
| dc.description.abstract | 隨著虛擬實境與3D列印技術的普及，人們對於三維模型的需求正在增加當中。本研究致力於發展一套能夠自動導航建立周圍三維環境模型的機器人系統，機器人系統由空間資訊收集裝置與機器人移動平台所構成。機器人移動平台負責進行自動導航任務，本研究利用即時定位與建構地圖(simmultaneous localization and mapping, SLAM)所產生的地圖與位置，同時配合A*路徑規劃演算法，以達成自動路徑規劃功能。空間資訊收集裝置負責進行場景收集任務，同時收集周圍空間環境的三維資訊與色彩資訊。本研究的場景接合演算法採用color supported GICP演算法，相對於傳統點對點架構的ICP演算法，color supported GICP演算法使用面對面的架構，更能夠解決三維點雲資料是表面取樣點的問題以及非完全重疊點雲的假設，並且利用額外的色彩資訊，可以加速收斂計算。本研究結果顯示，GICP在較少對應點時可以得到和ICP演算法相近的收斂誤差。color supported GICP色彩資訊的權重比例大約在0.2左右可以達最快的收斂速度；並且相較於沒有使用顏色作為輔助資訊的GICP演算法，平均而言收斂次數快了14次，也就是收斂速度快了60.9%(14/23)。本研究根據室內外以及機器人路徑設計規劃實驗，前往室外四個場景、室內兩個場景進行實驗。因為室內外場景尺度不同的關係，室外場景間最佳的最大搜尋半徑為0.05 m；室內場景間最佳的最大搜尋半徑為0.001 m。本研究分別測試ICP、color supported ICP、GICP、color supported GICP在各個案例中的表現。發現室外場景具有許多樹叢或破碎的點雲資料會造成GICP的誤判，使其收斂速度降低；於室內場景中，結果顯示GICP收斂的速度明顯比ICP來得快。然而所有案例都顯示color supported GICP可以利用顏色輔助以達到快速收斂的效果。儘管ICP和color supported ICP收斂的誤差可以比GICP來得小，但是在室內情況下GICP和color supported GICP的收斂速度明顯較快。 | zh_TW |
| dc.description.abstract | With the increasing popularity of 3D printing and rapid development of virtual reality, there is a large demand for good 3D model reconstruction mothed. This research aims to develop a simultaneous localization and mapping(SLAM)-based autonomous navigation scene reconstruction robot system. The system consists of a robot moving platform and a spatial information collector. The robot moving platform performs the SLAM-based autonomous navigation. By inputing the location information and map from SLAM, the A* path planning algorithm can achieve autonomous navigation. Spatial information collector collects the 3D spatial information and the color information simultaneously. This research adopts the color supported generalized iterative closest point (color supported GICP) method as the scene registration algorithm for 3D model reconstruction. Compared to classic point-to-point frame iteractive closest point (ICP), the color supported GICP is plane-to-plane frame approach which is designed to solve the sampling point cloud mismatching problem and the violation of fully overlapped region hypothesis. With extra color information, the color supported GICP can converge faster than ones that do not integrate color information. The experimental results show that GICP are capable of converging to nearly the same error that ICP does with fewer corresponding points. The color supported GICP reaches the highest converge speed when the weight of color information is 0.2. The extra color information can help the color supported GICP converging faster than the GICP with 14 less iterations or 60.9% faster time. There are 2 indoor scenes and 4 outdoor scenes tested in this research. As indoor and outdoor scenes have different scale, the best minimum search radius of outdoor case is 0.05 m, and the best minimum search radius of indoor case is 0.001 m. This research tests the performance of the 4 algorthms (ICP, color supported ICP, GICP, and color supported GICP) in these scenes. The result shows that bushes and fractured point clouds will slow down the converge speed of GICP. But in indoor cases, GICP converges faster than ICP. All the cases show that color supported GICP converges fastest among 4 algorithms. Although ICP and color supported ICP can converge to lower error than GICP and color supported GICP, GICP and color supported GICP converge faster than ICP and color supported ICP in indoor cases. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T16:10:55Z (GMT). No. of bitstreams: 1 ntu-104-R02631019-1.pdf: 10403376 bytes, checksum: ec01da26cd5f5b8dc51320df8f6bbbf2 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 誌謝 .................................................................................................................................. i
中文摘要 .......................................................................................................................... ii Abstract ............................................................................................................................ iii 目錄 ................................................................................................................................. v 圖目錄 ............................................................................................................................. ix 表目錄 ............................................................................................................................ xii 第一章緒論 .......................................................................................................... 1 1.1 前言 .......................................................................................................... 1 1.2 研究目的 .................................................................................................. 2 第二章文獻探討 .................................................................................................. 5 2.1 空間資訊擷取裝置 .................................................................................. 5 2.1.1 雷射掃描測距儀(Laser Scanner) ..................................................... 6 2.1.2 感測器融合(Sensor Fusion) ............................................................. 7 2.2 機器人即時定位與地圖建構(SLAM) .................................................... 9 2.2.1 貝氏濾波器(Bayes Filter) .............................................................. 12 2.2.2 Extended Kalman Filter SLAM(EKF SLAM) ............................... 13 2.2.3 Scan Matching SLAM .................................................................... 13 2.2.4 Graph-based SLAM ........................................................................ 14 2.3 機器人導航(Navigation) ........................................................................ 14 2.3.1 路徑規劃(Path Planning) ............................................................... 15 2.4 場景接合(Scenes Registration) .............................................................. 16 2.4.1 Iterative Closest Point(ICP) ............................................................ 16 2.4.2 ICP Variants .................................................................................... 18 2.4.3 Generalized ICP(GICP) .................................................................. 20 vi 2.4.4 Color Supported Generalized ICP .................................................. 22 2.5 表面重建(Surface Reconstruction) ........................................................ 23 2.5.1 Polygonal Mesh Reconstruction Algorithms .................................. 24 2.5.2 Normal Estimation .......................................................................... 25 2.5.3 Poisson Surface Reconstruction ..................................................... 25 第三章材料與方法 ............................................................................................ 26 3.1 系統架構 ................................................................................................ 26 3.1.1 硬體架構 ........................................................................................ 27 3.1.2 軟體架構 ........................................................................................ 33 3.2 即時定位與地圖建構(SLAM) .............................................................. 36 3.2.1 EKF SLAM .................................................................................... 37 3.2.2 機器人模型(Motion Model) .......................................................... 38 3.2.3 感測器模型(Measurement Model) ................................................ 41 3.3 自動導航 ................................................................................................ 42 3.3.1 最小凸邊形終點原則 .................................................................... 43 3.3.2 A*路徑規劃 ................................................................................... 44 3.4 空間資訊收集 ........................................................................................ 45 3.5 場景重建 ................................................................................................ 47 3.5.1 深度影像 ........................................................................................ 48 3.5.2 雷射掃描測距儀深度資訊座標轉換 ............................................ 49 3.5.3 雷射掃描測距儀與相機校正 ........................................................ 50 3.5.4 具有色彩之空間點雲 .................................................................... 51 3.6 多場景接合 ............................................................................................ 54 3.7 封閉迴路修正 ........................................................................................ 54 3.8 實驗規劃與方法 .................................................................................... 55 第四章結果與討論 ............................................................................................ 58 vii 4.1 馬達編碼器回歸實驗 ............................................................................ 58 4.2 即時定位與地圖建構(SLAM) .............................................................. 58 4.2.1 線特徵點截取 ................................................................................ 59 4.2.2 角特徵點截取 ................................................................................ 61 4.2.3 EKF SLAM 之實現 ....................................................................... 63 4.3 機器人自動導航 .................................................................................... 64 4.3.1 機率格點地圖 ................................................................................ 64 4.3.2 A*路徑規劃結果 ........................................................................... 65 4.4 單一場景重建 ........................................................................................ 67 4.4.1 雷射掃描測距儀與相機校正 ........................................................ 67 4.4.2 雷射資料深度影像(Range Image) ................................................ 71 4.4.3 三維點雲資料 ................................................................................ 72 4.4.4 色彩點雲資料 ................................................................................ 72 4.5 多場景接合 ............................................................................................ 73 4.5.1 兩兩場景間接合(Pairwise) ............................................................ 73 4.5.2 封閉迴路接合(Close Loop) ........................................................... 74 4.6 大尺度虛擬實境展示 ............................................................................ 76 4.7 場景接合演算法分析 ............................................................................ 77 4.7.1 最大搜尋半徑實驗分析 ................................................................ 78 4.7.2 色彩資訊權重的影響 .................................................................... 80 4.7.3 色彩資訊輔助 ................................................................................ 83 4.8 室外場景案例研究 ................................................................................ 85 4.8.1 臺大生機系農機館旁鐵皮屋(向內口字形路徑) ......................... 85 4.8.2 臺大體育館(向外口字形路徑)...................................................... 92 4.8.3 臺大總圖書館前(單側建築物一字形路徑).................................. 95 4.8.4 知武館與二號館間道路(兩側建築物一字形路徑)...................... 98 viii 4.9 室內場景案例研究 .............................................................................. 102 4.9.1 知武館207(向外口字形路徑)..................................................... 102 4.9.2 知武館四樓走廊(一字形路徑).................................................... 105 第五章結論與建議 .......................................................................................... 109 5.1 結論 ...................................................................................................... 109 5.2 建議 ....................................................................................................... 111 參考文獻 ....................................................................................................................... 112 | |
| dc.language.iso | zh-TW | |
| dc.title | 基於SLAM自動導航及色彩點雲演算法之大尺度場景重建方法 | zh_TW |
| dc.title | A Method of Large-Scale 3D Scene Reconstruction Using SLAM-Based Autonomous Navigation and Color Supported Point Cloud Algorithms | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 艾群(Chyung Ay),江昭皚(Joe-Air Jiang) | |
| dc.subject.keyword | 即時定位與地圖建構、最近點疊代演算法、色彩支持通用最近點疊代演算法、場景重建、場景接合, | zh_TW |
| dc.subject.keyword | Simultaneous localization and mapping (SLAM), iterative closest point (ICP), color supported generalized iterative closest point (color supported GICP), scene reconstruction, scene registration, | en |
| dc.relation.page | 89 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-08-18 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物機電工程學系 | |
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