跳到主要內容

臺灣博碩士論文加值系統

(18.97.14.86) 您好!臺灣時間:2025/02/07 21:57
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:陳世軒
研究生(外文):Shih-Hsuan Chen
論文名稱:應用 SLAM 自走機器人自動建置場域 Google 街景地圖
論文名稱(外文):Application of SLAM-based Autonomous Robot for Building Custom Field Street View in Google Maps
指導教授:林達德林達德引用關係
指導教授(外文):Ta-Te Lin
口試日期:2017-06-29
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生物產業機電工程學研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:146
中文關鍵詞:即時定位與地圖建構最近疊代點場景重建場景接合立體視覺雷達感測器感測器融合障礙物偵測避障系統
外文關鍵詞:Simultaneous localization and mapping (SLAM)iterative closest point (ICP)scene reconstructionstereo visionradarsensor fusionobstacle detectionobstacle avoidance
相關次數:
  • 被引用被引用:1
  • 點閱點閱:753
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
本研究致力於發展一套自動化的街景拍攝系統。為達成此目的所開發之系統中搭載了許多不同的感測器,各個感測器各司其職,在機器人視覺領域中,即時定位與場景建構SLAM 是一項能夠針對未知環境探索時所採用的技術,透過重複的觀測以及控制的資訊,能夠估計出環境的資訊以及機器人的姿態。立體視覺是由兩個攝影機所組成的感測器,能夠透過影像處理以及立體視覺理論能夠計算出影像的空間資訊,缺點是容易受到環境狀態像是光源、成像品質、距離影響深度距離的計算,因此加入雷達感測器進行輔助,其透過都卜勒效應的原理來量測感測器與物體的距離及其他資訊,透過立體視覺與雷達感測器利用感測器融合的方式提高量測的可靠度,藉由感測器融合能夠提供系統進行障礙物偵測、障礙物追蹤,甚至提供系統利用 A* 演算法進行路徑規劃達到閃躲的能力。機器人在行走的狀況下容易因為路面品質不同、輪胎的打滑造成移動時會產生不同程度的誤差,本研究利用 SLAM 得到機器人的姿態提供給予 PID 控制器作為回授訊號,讓機器人在移動的過程中隨時修正路線。在室內及戶外的情況下,實驗得到直行之平均偏移量分別為 1.7 cm 及 2.0 cm。GPS (Global Positioning System) 是透過衛星定位出當下的位置,其因不同環境下有數公尺到數十公尺不等的誤差,本研究透過 SLAM 的方式搭配特徵點的選取藉此推算出拍攝當下實際之位置,與 GPS 所記錄資訊相較之下能夠大大提高精確度。本研究能夠於自動導航並拍攝街景資訊,將影像中的 GPS 資訊經過 SLAM 進行座標校正後,街景能夠上傳至公開的雲端平台上讓世界上各個角落的使用者能夠觀看到當地的場景資訊,或是儲存至私人的後端資料庫中方便使用者進行觀察以及記錄當時的環境資訊。
This study is dedicated to the development of an automated street shooting system. In order to achieve this purpose, the system is equipped with a number of different sensors with different usage. In the field of robot vision, SLAM (Simultaneous localization and mapping) is a technology be used to explore the unknown environment, can estimate the information of the environment and the robot''s location through repetitive observation and controlled information. Stereo vision system, which is composed of two cameras, can calculate the spatial information of the image through image processing and stereo vision theory. The disadvantage is that the accuracy of the depth of the distance calculation is easily affected by the environment such as light source, image quality, and distance. Therefore, we add the radar, which measure the distance between the sensor and the object and other related information through the Doppler effect principle. To improve the overall reliability of the measurement through the stereo vision and radar sensor using the sensor fusion approach. The results obtained by the sensor fusion can provide obstacle detection, obstacle tracking, and even provide the system to do A * path planning algorithm to dodge them. The robot will have different degrees of error because of the different quality of the road or the tire slip caused during the movement. In order to fix this, this study use SLAM to provide the location of robot to gain PID control feedback signal, which can give the robot the ability to correct the route in the process of moving indoor and outdoor. The results of the experiment get the average offset of 0.084 m and 0.125m.GPS can locate the current position through satellite, with an error from a few meters to tens of meters due to the environment. This study combines SLAM with the selection of feature points to calculate the actual location of the shooting, and the accuracy compared with the information recorded by GPS is greatly improved. This study can automatically navigate and capture the scene information on the general road or in a specific field. After the GPS information of the image is corrected by SLAM, the 360 ° panoramic image can be uploaded to the open cloud platform to let the worldwide users view local scene information or store them in a private back-end database, which make it easier for users to view and record environmental information at the time.
誌謝 i
中文摘要 ii
Abstract iii
目錄 v
圖目錄 ix
表目錄 xiv
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 6
2.1 立體視覺 6
2.1.1 攝影機校正 6
2.1.2 立體視覺理論 7
2.1.3 對應點匹配 8
2.1.4 深度資訊的計算 10
2.1.5 障礙物偵測 11
2.2 雷達感測器 13
2.3 感測器融合 15
2.4 自走機器人導航 17
2.5 避障系統 19
2.5.1 障礙物追蹤 19
2.5.2 撞擊預先警示系統 20
2.5.3 路徑規劃 21
2.6 雷射測距儀 (Laser Range Finder) 22
2.7 機器人同步定位與地圖建構 23
2.7.1 Online SLAM 與 Full SLAM 24
2.7.2 Extended Kalman Filter SLAM (EKF SLAM) 25
2.7.3 Scan Matching SLAM 27
2.7.4 RBPF SLAM 27
2.7.5 Graph-Based SLAM 28
2.8 場景接合 (Scenes Registration) 29
2.8.1 最近疊代點 Iterative Closet Point (ICP) 29
2.8.2 ICP Variants 30
第三章 材料與方法 32
3.1 系統架構 32
3.1.1 硬體架構 33
3.1.2 軟體架構 41
3.1.3 圖形化介面 45
3.2 立體視覺 46
3.2.1 攝影機校正 46
3.2.2 深度資訊的計算 49
3.2.3 障礙物偵測 49
3.3 雷達感測器 50
3.4 感測器座標轉換 50
3.5 感測器融合 52
3.6 障礙物追蹤 53
3.6.1 障礙物特徵匹配 53
3.6.2 卡爾曼濾波器 54
3.7 即時定位與地圖建構 (SLAM) 55
3.7.1 ICP (Iterative Closet Point) SLAM 56
3.7.2 機器人模型 57
3.8 自動導航與自走車控制 59
3.8.1 自動導航 59
3.8.2 自走車控制 60
3.9 路徑規劃與緊急煞車系統 64
3.9.1 路徑規劃 64
3.9.2 緊急煞車系統 66
3.10 GPS 距離計算與校正 67
3.11 實驗資料搜集 68
3.12 實驗規劃與方法 69
第四章 結果與討論 71
4.1 立體視覺與雷達感測器之感測器融合性能評估 71
4.2 障礙物避障 74
4.2.1 煞車性能測試 75
4.2.2 緊急煞車實驗 77
4.3 測試系統誤差 78
4.3.1 馬達編碼器迴歸實驗 79
4.3.2 直線移動距離誤差測量實驗 80
4.3.3 直線移動角度誤差測量實驗 82
4.3.4 開路控制與 PID 控制之比較 83
4.4 即時定位與地圖建構SLAM 91
4.4.1 建構室內地圖 91
4.4.2 建構室外地圖 93
4.4.3 編碼器軌跡與 SLAM 修正後軌跡之比較 101
4.5 封閉迴路接合實驗 104
4.6 導航系統 106
4.7 GPS 校正 108
4.8 戶外場景案例研究 118
4.8.1 台大綜合體育館前廣場 118
4.8.2 台大文學院前廣場 122
4.8.3 台灣大學總圖書館前廣場 124
4.8.4 台大生機系館 128
4.9 圖形化介面及成果 131
4.9.1 SLAM 程式 132
4.9.2 GPS 位置校正程式 133
4.9.3 街景服務程式 134
第五章 結論與建議 137
5.1 結論 137
5.2 建議 139
參考文獻 141
劉昶志。2011。基於擴展式卡爾曼濾波器織田間機器人即時同步定位與地圖建構演算法。碩士論文。臺北:國立臺灣大學生物產業機電工程學研究所。

賴宗誠。2012。應用多組雙眼攝影機系統進行車前三維環境模型重建。碩士論文。臺北:國立臺灣大學生物產業機電工程學研究所。

莊凱強。2013。基於立體視覺之即時障礙物追蹤與避障方法。碩士論文。臺北:國立臺灣大學生物產業機電工程學研究所。

李榕修。2015。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。碩士論文。臺北:國立臺灣大學生物產業機電工程學研究所。

翁立剛。2015。立體視覺與雷達感測器融合系統於車輛避障之應用。碩士論文。臺北:國立臺灣大學生物產業機電工程學研究所。

Alessandretti, G., A. Broggi and P. Cerri. 2007. Vehicle and guard rail detection using radar and vision data fusion. IEEE Transactions on Intelligent Transportation Systems. 8(1), 95-105.

Arulampalam, M. S., S. Maskell, N. Gordon and T. Clapp. 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing. 50(2), 174-188.

Arun, K. S., T. S. Huang, and S. D. Blostein. 1987. Least-Squares Fitting of Two 3-D Point Sets. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9(5):698-700.

Bernini, N., M. Bertozzi, L. Castangia, M. Patander and M. Sabbatelli. 2014. Real-time obstacle detection using stereo vision for autonomous ground vehicles: A survey. Paper presented at the Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on.

Besl, P. J., and N. D. McKay. 1992. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2):239-256.

Bouguet, J.-Y. 2004. Camera calibration toolbox for matlab.

Boykov, Y., O. Veksler, and R. Zabih. 2001. Fast Approximate Energy Minimization via Graph Cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11):1222-1239.

Chen, Y. L., M. R. Jahanshahi, P. Manjunatha, W. Gan, M. Abdelbarr, S. F. Masri, B. Becerik-Gerber, and J. P. Caffrey. 2016. Inexpensive Multimodal Sensor Fusion System for Autonomous Data Acquisition of Road Surface Conditions. IEEE Sensors Journal 16(21):7731-7743.

Collins, T. 2004. Graph cut matching in computer vision. University of Edinburgh

DeSouza, G. N., A. H. Jones, and A. C. Kak. 2002. An world-independent approach for the calibration of mobile robotics active stereo heads. In Robotics and Automation, 2002. Proceedings. ICRA ''02. IEEE International Conference on.

Dijkstra, E. W. 1959. A note on two problems in connexion with graphs. Numerische mathematik. 1(1), 269-271.

Dissanayake, M. W. M. G., P. Newman, S. Clark, H. F. Durrant-Whyte, and M.

Doucet, A., N. d. Freitas, K. P. Murphy, and S. J. Russell. 2000. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. 720075: Morgan Kaufmann Publishers Inc.

Gong, J., L. Li and W. Chen. 1998. Fast recursive algorithms for two-dimensional thresholding. Pattern Recognition. 31(3), 295-300.

Greenspan, M., and M. Yurick. 2003. Approximate k-d tree search for efficient ICP. In Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings.

Harrison, A., and P. Newman. 2008. High quality 3D laser ranging under general vehicle motion. In 2008 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. IEEE Transactions on Systems Science and Cybernetics. 4(2), 100-107.

Hackett, J. K. and M. Shah. 1990, 13-18 May 1990. Multi-sensor fusion: a perspective. Paper presented at the Proceedings of the 1990 IEEE International Conference on Robotics and Automation.

Hasch, J., E. Topak, R. Schnabel, T. Zwick, R. Weigel, and C. Waldschmidt. 2012. Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band. IEEE Transactions on Microwave Theory and Techniques 60(3):845-860.

Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Journal of Fluids Engineering. 82(1), 35-45.

Labayrade, R., C. Royere, D. Gruyer and D. Aubert. 2005. Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner. Autonomous Robots. 19(2), 117-140.

Leonard, J. J., and H. F. Durrant-Whyte. 1991. Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation 7(3):376-382.

Levenberg, K. 1944. A metod for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics 2(2):164-168.

Lin, K., C. Chang, A. Dopfer and C. Wang. 2012. Mapping and Localization in 3D Environments Using a 2D Laser Scanner and a Stereo Camera. Journal of Information Science and Engineering. 28(1), 131-144.

Men, H., B. Gebre, and K. Pochiraju. 2011. Color point cloud registration with 4D ICP algorithm. In 2011 IEEE International Conference on Robotics and Automation.
Pocol, C., S. Nedevschi and M.-M. Meinecke. 2008. Obstacle detection based on dense stereovision for urban ACC systems. Paper presented at the Proceedings of 5th International Workshop on Intelligent Transportation (WIT 2008).

Rusinkiewicz, S., and M. Levoy. 2001. Efficient variants of the ICP algorithm. In Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

Scharstein, D. and R. Szeliski. 2002. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International journal of computer vision. 47(1-3), 7-42.

Thrun, S., W. Burgard and D. Fox. 2005.Probabilistic robotics

Smith, R. C., and P. Cheeseman. 1986. On the Representation and Estimation of Spatial Uncertainty. The International Journal of Robotics Research 5(4):56-68.

Stentz, A. 1994. Optimal and efficient path planning for partially-known environments.
IEEE International Conference on Robotics and Automation.

Sun, Z., G. Bebis and R. Miller. 2006. On-road vehicle detection: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(5), 694-711.

Wang, T., N. Zheng, J. Xin and Z. Ma. 2011. Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications. Sensors. 11(9), 8992-9008.

Wu, S., S. Decker, P. Chang, T. Camus and J. Elefath. 2009. Collision Sensing by Stereo Vision and Radar Sensor Fusion. IEEE Transactions on Intelligent Transportation Systems. 10(4), 606-614.

Yifeng, N., Z. Zhiwei, Z. Daibing, W. Xun, and L. Jianhong. 2016. An approach to ground target localization for UAVs based on multi-sensor fusion. In 2016 12th World Congress on Intelligent Control and Automation (WCICA).

Yilmaz, A., O. Javed and M. Shah. 2006. Object tracking: A survey. Acm computing surveys (CSUR). 38(4), 13.

Zhengyou, Z. 2000. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(11), 1330-1334.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊