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研究生:顏嘉佑
研究生(外文):Chia-Yo Yen
論文名稱:基於點雲比對與視覺伺服之自動化物件夾取系統
論文名稱(外文):Automated Object Grasping Based on Point Cloud Matching and Visual Servoing
指導教授:張文中
指導教授(外文):Wen-Chung Chang
口試委員:張政元王銀添胡竹生姚立德
口試日期:2016-07-25
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
語文別:中文
中文關鍵詞:點雲比對、視覺伺服、疊代最近點、物件夾取。
外文關鍵詞:Point Cloud MatchingVisual ServoingIterative Closet PointObject Grasping.
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本論文提出一種基於點雲比對與視覺伺服之自動化物件夾取系統,藉由三維攝影機取得實際物件之當前單一視角的資料點雲後,透過本論文所提出的SU-SAC以及SU-ICP演算法快速比對資料點雲與完整模型點雲,進而計算出點雲之間的姿態轉換關係,再透過環境中的監視攝影機求出機械手臂基底與三維攝影機的座標系統轉換關係,運用此關係將資料點雲轉換至機械手臂座標系統下,透過逆向運動學控制機械手臂移動至大略夾取位置,接下來運用三維攝影機即時觀察手臂末端夾爪與物件的姿態,以閉迴路視覺伺服控制將機械手臂末端夾爪精確移動至物件的夾取位置,完成自動化物件夾取任務。本論文所提出之點雲比對方法為運用基於曲率變化的點雲稀疏化方法,改善現有之採樣一致性(SAC)以及最近點疊代匹配法(ICP)計算效能。本系統之點雲比對方法與自動化物件夾取系統已經實驗驗證其可行性及有效性。
An automated object grasping system based on point cloud matching and visual servoing is presented in the thesis. The point cloud of a real object from a single viewing angle can be captured by a 3D camera. Based on the proposed SU-SAC algorithm and the SU-ICP approach, the data point cloud and the complete model point cloud can be matched rapidly. The transformation between the manipulator base frame and the 3D camera frame can be estimated through a surveillance camera fixedly mounted in the environment. Then, the transformation from the data point cloud frame to the manipulator base frame can be computed. Then manipulator can thus be driven to the grasping pose approximately. Since the poses of the gripper of the manipulator and the object can both be observed by the 3D camera, a closed-loop visual servoing approach can be employed to accomplish the automated grasping task with precision. The Speed-Up approach proposed in the thesis is based on the curvature of point clouds. The matching efficiency of existing SAmple Consensus (SAC) and Iterative Closest Point (ICP) algorithm can be improved by the Speed-Up approach. The feasibility and effectiveness of the proposed point cloud matching and visual servoing approaches have been validated by experimenting with the automated object grasping system.
中文摘要 ............................................................................. i
英文摘要 ............................................................................. ii
誌謝................................................................................... iii
目錄................................................................................... iv
表目錄 ................................................................................ v
圖目錄 ................................................................................ vi
第一章 緒論 ....................................................................... 1
1.1 研究動機及目的 .................................................... 1
1.2 文獻回顧 ............................................................ 1
1.3 論文具體成果....................................................... 2
1.4 論文章節瀏覽....................................................... 3
第二章 系統簡介 .................................................................. 4
2.1 系統架構 ............................................................ 4
2.2 系統流程 ............................................................ 6
第三章 現有三維點雲比對方法 .................................................. 8
3.1 ICP演算法之精密剛性比對 ........................................ 8
3.2 Sample Consensus-Initial Alignment(SAC-IA) 演算法 ......... 11
3.2.1 Point Feature Histograms(PFH)................................. 12
3.2.2 Fast Point Feature Histograms(FPFH).......................... 13
第四章 三維點雲比對效率之優化技術........................................... 15
4.1 三維點雲處理....................................................... 16
4.1.1 三維點雲雜訊去除................................................ 16
4.1.2 網格稀疏化 ....................................................... 17
4.2 基於曲率之點雲稀疏化 (Speed-Up, SU).......................... 18
4.3 大略姿態估測 (Approximate Pose Estimation) .................. 20
4.4 SU-SAC演算法..................................................... 22
4.5 SU-ICP演算法...................................................... 23
第五章 視覺伺服之物件夾取任務................................................ 25
5.1 建立座標系統....................................................... 25
5.1.1 物件期望座標系統................................................ 25
5.1.2 手臂夾爪當前座標系統 .......................................... 26
5.2 建立三維攝影機與手臂基底座標系統之轉換關係................ 28
5.2.1 監視攝影機與三維攝影機的坐標系統轉換...................... 28
5.2.2 監視攝影機與機械手臂基底的坐標系統轉換 ................... 32
5.2.3 機械手臂基底與三維攝影機的轉換關係 ........................ 33
5.3 Cartesian-based視覺伺服控制器 .................................. 33
第六章 實驗結果 .................................................................. 38
6.1 實驗規劃 ............................................................ 38
6.2 實驗設備 ............................................................ 39
6.2.1 實驗測試物件..................................................... 41
6.2.2 實驗測試環境..................................................... 42
6.2.3 三維點雲處理..................................................... 43
6.2.4 基於曲率之點雲稀疏化閥值評比 ................................ 44
6.3 三維點雲比對實驗結果 ............................................ 46
6.3.1 SU-SAC比對結果 ................................................ 48
6.3.2 SU-ICP比對結果 ................................................. 50
6.4 物件夾取任務結果.................................................. 53
6.5 實驗結果討論....................................................... 59
第七章 結論與未來展望 .......................................................... 61
7.1 結論 ................................................................. 61
7.2 未來展望 ............................................................ 61
參考文獻 ............................................................................. 63
附錄................................................................................... 67
A 作者簡介 .................................................................. 67
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