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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林達德(Ta-Te Lin) | |
| dc.contributor.author | Jhih-Syuan Shih | en |
| dc.contributor.author | 施志軒 | zh_TW |
| dc.date.accessioned | 2021-05-16T16:17:47Z | - |
| dc.date.available | 2013-08-26 | |
| dc.date.available | 2021-05-16T16:17:47Z | - |
| dc.date.copyright | 2013-08-26 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-16 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5850 | - |
| dc.description.abstract | 3D 虛擬場景已被廣泛應用於許多場合，如虛擬實境與地理資訊系統等應用。本研究致力於發展一套低成本、能自動重建大尺度場景之自主式移動機器人。為達到此目的機器人必須搭載自行開發之環境資訊收集裝置 (environment information collector, EIC) 來收集環境三維空間資訊與影像資訊。並且採用以擴展式卡曼濾波器為基礎的同步定位與地圖建構技術 (simultaneous localization and mapping, SLAM) 來估測機器人自身位置並連結各場景間之關係。將所收集到的深度與影像資訊分別存放於深度影像與環場影像中，在透過兩張影像間之輪廓對應降低傳統雷射與影像間對應的繁雜手續，加快色彩點雲場景的重建。多場景間的接合是利用疊代最接近點演算法 (iterative closest points, ICP) 來完成，但為了避免地形起伏所造成的影響，故考慮機器人六個方向自由度的姿態來進行場景接合。在完成場景接合後仍需透過三角網格處理建立模型表面，並運用材質貼圖來產生高品質的場景模型。本系統提出改良後的 ICP 演算法來提升場景接合之成功率與降低運算時間，平均單步執行時間為7.9秒。場景與場景之平均距離為10.6 公尺，且接合誤差小於8%。若在接合時偵測到封閉迴路，將利用 ELCH (explicit loop closing heuristic) 演算法來加以修正。 | zh_TW |
| dc.description.abstract | 3D virtual scenes have been extensively applied in many fields such as virtual reality and geographic information system. The aim of our research is to develop a low-cost autonomous mobile robot which reconstructs large-scale scenes automatically. To achieve this goal, our robot is equipped with an environment information collector which acquires 3D space and image information of the environment. The EKF-based SLAM was applied to estimate the robot position and link different scenes. The range and color information was saved in panoramic images and range images respectively. The contour projection of these two images had replaced the complex matching procedures between laser points and image and speeded up color point cloud scene reconstruction. Multiple scene registration was done by applying the ICP algorithm. However, in order to avoid the effect of bumpy ground, the robot pose considered six degree of freedom (DoF). After the scene registration, the surface model was constructed by triangular mesh processing. Utilizing the texture mapping has produced high quality scene models. Our system proposed a modified ICP algorithm to improve the success rate and running time of scenes registration. The average running time was 7.9 seconds per step and the average distance between each scene was about 10.6 meters. The error in registration was less than 8%. When the loop closure was detected in a scene registration, the ELCH algorithm was applied to amend it. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-16T16:17:47Z (GMT). No. of bitstreams: 1 ntu-102-R00631007-1.pdf: 14379739 bytes, checksum: 30c308d19b4a25d33b81fe5bbf432dbc (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii Abstract iii 目 錄 iv 圖目錄 viii 表目錄 xii 第一章 前言與研究目的 1 1.1 前言 1 1.2 研究目的 3 第二章 文獻探討 6 2.1 機率機器人學 (Probabilistic Robotics) 6 2.1.1 基礎架構 6 2.1.2 貝式濾波器 (Bayes Filter) 8 2.2 機器人同步定位與地圖建構 9 2.2.1 Online SLAM 與 Full SLAM 10 2.2.2 Extended Kalman Filter SLAM (EKF SLAM) 11 2.2.3 Scan Matching SLAM 13 2.3 機器人導航 (Navigation) 16 2.3.1 路徑規劃 (Path Planning) 17 2.3.2 障礙物迴避 (Obstacle Avoidance) 19 2.4 三維空間資訊擷取裝置 21 2.4.1 立體視覺相機 (Stereo Vision Camera) 21 2.4.2 雷射測距儀 (Laser Range Finder) 24 2.4.3 感測器融合 (Sensor Fusion) 26 2.5 虛擬實境 (Virtual Reality) 28 2.5.1 大尺度虛擬實境 (Large-scale Virtual Reality ) 30 2.5.2 地理資訊系統 (Geographic Information System) 33 第三章 材料與方法 35 3.1 系統架構 35 3.1.1 硬體架構 36 3.1.2 軟體架構 43 3.2 機器人模型 46 3.2.1 空間座標系統描述 46 3.2.2 機器人運動模型 49 3.2.3 機器人感測器模型 52 3.3 空間與影像資料收集 54 3.3.1 行人偵測 56 3.3.2 深度影像處理 60 3.3.3 環場影像接合 63 3.4 機器人同步定位與地圖建構 66 3.4.1 EKF SLAM 66 3.4.2 特徵點擷取 71 3.4.3 特徵點篩選與資料關聯性檢索 75 3.5 機器人運動規劃 78 3.5.1 機率格點地圖建構 78 3.5.2 路徑規劃 79 3.5.3 導航 80 3.6 場景重建 83 3.6.1 單一場景重建 85 3.6.2 多場景接合 89 3.7 大尺度虛擬實境場景建立 98 3.7.1 表面重建與孔隙填補 99 3.7.2 虛擬實境場景展示 102 3.8 實驗規劃 104 第四章 結果與討論 106 4.1 機器人機構設計 106 4.2 空間與影像資料收集 110 4.2.1 深度影像處理結果 110 4.2.2 環場影像接合結果 114 4.3 EKF SLAM 115 4.3.1 特徵點擷取 115 4.3.2 EKF SLAM 演算法之實現 118 4.4 機器人運動規劃 119 4.4.1 機率格點地圖之實現 119 4.4.2 A*路徑規劃結果 120 4.5 場景重建 121 4.5.1 單一場景重建結果 121 4.5.2 多場景接合結果比較 125 4.5.3 封閉迴路修正 132 4.6 大尺度虛擬實境場景建立 135 4.6.1 表面重建與分析 135 4.6.2 材質貼圖結果 140 4.7 個案研究 144 4.7.1 臺灣大學人工氣候室 144 4.7.2 臺灣大學綠房子 151 第五章 結論與建議 156 5.1 結論 156 5.2 建議 158 參考文獻 159 | |
| dc.language.iso | zh-TW | |
| dc.title | 以SLAM機器人建立三維大尺度場景之演算法研究 | zh_TW |
| dc.title | A Study on 3D Large-Scale Scenes Reconstruction Algorithms Using a SLAM Robot | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王傑智(Chieh-Chih Wang),顏炳郎 | |
| dc.subject.keyword | 同步定位與地圖建構,感測器融合,場景重建,材質貼圖, | zh_TW |
| dc.subject.keyword | Simultaneous localization and mapping (SLAM),Sensor fusion,Scene reconstruction,Texture mapping, | en |
| dc.relation.page | 165 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2013-08-17 | |
| 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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