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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 連豊力(Feng-Li Lian) | |
| dc.contributor.author | Chung-Chien Shen | en |
| dc.contributor.author | 沈鍾潛 | zh_TW |
| dc.date.accessioned | 2021-06-15T13:43:15Z | - |
| dc.date.available | 2019-02-15 | |
| dc.date.copyright | 2016-02-15 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-12-22 | |
| dc.identifier.citation | Papers:
[1: Choi et al. 2005] Y.-H. Choi and S.-Y. Oh, “Visual sonar based localization using particle attraction and scattering,” in Proceedings of IEEE International Conference on Mechatronics and Automation, Niagara Falls, Canada, pp. 449-454, Jul. 1- Aug. 29, 2005. [2: Menegatti et al. 2006] E. Menegatti, A. Pretto, A. Scarpa, and E. Pagello, “Omnidirectional vision scan matching for robot localization in dynamic environments,” IEEE Transactions on Robotics, Vol. 22, No. 3, pp. 523-535, Jun. 2006. [3: Menegatti et al. 2000] E. Menegatti, A. Pretto, A. Scarpa and E. Pagello, “Vision-based navigation and environmental representations with an omnidirectional camera,” IEEE Transactions on Robotics, Vol. 22, No. 3, pp. 890-898, Dec. 2000. [4: Scaramuzza & Siegwart 2008] D. Scaramuzza and R. Siegwart, “Appearance-guided monocular omnidirectional visual odometry for outdoor ground vehicles,” IEEE Transactions on Robotics, Vol. 24, No. 5, pp. 1015-1026, Oct. 2008. [5: Gong et al. 2007] X. Gong, A. Subramanian, C. L. Wyatt and D. J. Stilwell, “Performance analysis and validation of a paracatadioptric omnistereo system,” in Proceedings of IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1-8, Oct. 14-21, 2007. [6: Scaramuzza et al. 2009] D. Scaramuzza, N. Criblez, A. Martinelli and R. Siegwart, “A robust descriptor for tracking vertical lines in omnidirectional images and its use in robot self-calibration,” International Journal on Robotics Research, Vol. 28, No. 2, pp. 1015-1026, Oct. 2009. [7: Li et al. 2009] M. Li, K. Imou, K. Wakabayashi and S. Yokoyama, “Localization system based on artificial landmark and omnidirectional vision,” in Proceedings of IEEE International Conference on Information Engineering and Computer Science, Wuhan, China, pp.1-4, Dec. 19-20, 2009. [8: Wongphati et al. 2009] M. Wongphati, N. Niparnan and A. Sudsang, “Bearing only FastSLAM using vertical line information from an omnidirectional camera, ” in Proceedings of IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, pp. 1188-1193, Feb. 22-25, 2009. [9: Geyer & Daniilidis 2001] C. Geyer and K. Daniilidis, “Catadioptric projective geometry,” International Journal of Computer Vision, Vol. 45, No. 3, pp.223-243, Dec. 2001. [10: Antone & Teller 2000] M. E. Antone and S. Teller, “Automatic recovery of relative camera rotations for urban scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 282-289, Jun. 13-15, 2000. [11: Bosse et al. 2002] M. Bosse, R. Rikoski, J. Leonard and S. Teller, “Vanishing points and 3d lines from omnidirectional video,” in Proceedings of IEEE International Conference on Image Processing, Rochester, New York, Vol.3, pp. 513- 516, Jun. 24-28, 2002. [12: Magnier et al. 2010] B. Magnier, F. Comby, O. Strauss, J. Triboulet and C. Demonceaux, “Highly specific pose estimation with a catadioptric omnidirectional camera,” in Proceedings of IEEE Conference on Imaging Systems and Techniques, Thessaloniki, Greece, pp. 229-233, Jul. 1-2, 2010. [13: Bazin et al. 2007] J. Bazin, I. Kweon and C. Demonceaux, “Rectangle extraction in catadioptric images,” in Proceedings of International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1-7, Oct. 14-21, 2007. [14: Bazin et al. 2008] J. Bazin, I. Kweon, C. Demonceaux and P. Vasseur, “A robust top-down approach for rotation estimation and vanishing points extraction by catadioptric vision in urban environment,” in Proceedings of International Conference on Intelligent Robots and Systems, Nice, France, pp. 22-26, Sep. 2008. [15: Bazin et al. 2012] J.-C. Bazin, C. Demonceaux, P. Vasseur, and I. Kweon, “Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment,” the International Journal of Robotics Research, Vol. 31, No. 1, pp. 63-81, Jan. 2012. [16: Bazin et al. 2010] J. C. Bazin, C. Demonceaux, P. Vasseur, and I. Kweon, “Motion Estimation by Decoupling Rotation and Translation in Catadioptric Vision,” in Computer Vision and Image Understanding, Vol. 114, No. 2, pp. 254-273, Feb. 2010. [17: Kim et al. 2011] S. Kim, J. Bazin, H. Lee, K. Choi and S. Park, 'Ground vehicle navigation in harsh urban conditions by integrating inertial navigation system, global positioning system, odometer and vision data, ' The Institution of Engineering and Technology Radar Sonar and Navigation, vol. 5, no. 8, pp. 814-823, Oct, 2011. [18: Bazin et al. 2010] J.-C. Bazin, P.-Y. Laffont, I. Kweon, C. Demonceaux, and P. Vasseur, “An original approach for automatic plane extraction by omnidirectional vision,” in Proceedings of IEEE International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 752-758, Oct. 18-22, 2010. [19: Ericson & Astrand 2010] S. Ericson and B. Astrand, “Row-detection on an agricultural field using omnidirectional camera,” in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 4982-4987, Oct. 18-22, 2010. [20: Puig et al. 2010] L. Puig, J. Bermudez and J.J. Guerrero, “Self-orientation of a hand-held catadioptric system in man-made environments,” in Proceedings of IEEE International Conference on Robotics and Automation Anchorage Convention District, Anchorage, Alaska, USA, pp. 2549-2555, May 3-8, 2010. [21: Choi et al. 2011] J. Choi, W. Kim, and H. Kong, and C. Kim, “Real-time vanishing point detection using the local dominant orientation signature,” 3DTV Conference: The True Vision – Capture, Transmission and Display of 3D Vide, pp.1-4, May 16-18, 2011. [22: Bonev et al. 2007] B. Bonev, M. Cazorla and F. Escolano, “Robot navigation behaviors based on omnidirectional vision and information theory,” Journal of Physical Agents, Vol. 1, No. 1, pp. 27-36, July 2007. [23: Scaramuzza et al. 2006] D. Scaramuzza and ,A. Martinelli and R. Siegwart, “A flexible technique for accurate omnidirectional camera calibration and structure from motion, ” in Proceedings of IEEE International Conference on Computer Vision Systems, New York, USA, pp. 45-52, Jan. 04-07, 2006. [24: Mei & Rivers 2007] C. Mei and P. Rives, “Single view point omnidirectional camera calibration from planar grids,” in Proceedings of IEEE International Conference on Robotics and Automation, Rome, Italy, pp. 3945-3950, Apr. 10-14, 2007. [25: Cao et al. 2007] Z. Cao, S. Liu and J. Roning, “Omni-directional vision localization based on particle filter,” in Proceedings of International Conference on Image and Graphics, Sichuan, China, pp. 478-483, Aug. 22-24, 2007. [26: Barreto & Araujo 2005] J. P. Barreto and H. Araujo, “Geometric properties of central catadioptric line images and their application in calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp. 1327-1333, Aug. 2005. [27: Ying & Hu 2004] X.H. Ying and Z.Y. Hu, 'Catadioptric camera calibration using geometric invariants,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No. 10, pp.1260-1271, Oct. 2004. [28: Geyer & Daniilidis 2001] C. Geyer and K. Daniilidis, “Catadioptric camera calibration,” in Proceedings of International Conference on Computer Vision, Sichuan, Kerkyra, Greece, pp. 398-404, Sep. 20-27, 1999. [29: Zhu et al. 2013] H. Zhu, X. Xu, J. Zhou and X. Wang, “Using vanishing points to estimate parameters of fisheye camera, ” The Institution of Engineering and Technology Computer Vision, Vol. 7, No. 5, pp. 362-372, Oct. 2013. [30: Scotti et al. 2005] G. Scotti, L. Marcenaro, C. Coelho, F. Selvaggi and C.S. Regazzoni, “Dual camera intelligent sensor for high definition 360 degrees surveillance,” IEE Proceedings Vision, Image and Signal Processing, Vol. 152, No. 2, pp. 250-257, Apr. 2005. [31: Yang 2010] J.-Y. Yang “Omnidirectional vision-based robot localization using vertical line matching and detection of vanishing point and floor region using edge orientation information,” 國立臺灣大學電機工程學研究所碩士論文, Jun. 2012. Websites: [32: Camera Calibration Toolbox for Matlab] Camera Calibration Toolbox for Matlab, [Online], Available: https://sites.google.com/site/scarabotix/ocamcalib-toolbox [33: Tracking Vertical Lines In omnidirectional Images for Matlab] Tracking Vertical Lines In omnidirectional Images, [Online], Available: http://rpg.ifi.uzh.ch/research_ocamfilter.html | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51658 | - |
| dc.description.abstract | 機器人或無人載具之自動導航越來越受重視，其與旋轉估測之正確性極為相關。全向相機相較傳統相機擁有更大的視野，可以抓取更多周遭環境中的視覺資訊，而使用全向影像中的垂直線資訊，供地面載具計算水平旋轉角度的方法已被提出。但在三維環境中，多種無人載具(如 UAV)並非僅行動於水平面上，故需提出三維空間中估測旋轉角度的方法，而環境中的消失點有助於判識機身的旋轉情況。本論文藉由全向相機所攝之全向影像，由中提取消失點以及估測旋轉情形。
本研究先以邊界偵測找出全向影像中的邊界點，並將這些邊界點匯集成連續線段，經由直線偵測與消失點提取之後，可得環境中多組線段對應之消失點的方向資訊，運用於後續旋轉估測分析。並藉由全向影像中的直線與現實空間中的直線對應關聯，發展全向相機校正模式，透過室內環境之多組全向影像試驗，與追蹤垂直線方法的結果進行探討。 | zh_TW |
| dc.description.abstract | Automatic navigation for mobile robots or unmanned vehicles becomes a more and more important ability, and this ability has a great deal to do with accurate rotation estimation. An omnidirectional camera, with a wider field of view compared with a traditional camera, can catch more visual information from the surroundings. The method using the information vertical lines in omnidirectional images is proposed to calculate two-dimensional rotation angles of the camera. But in three-dimensional environment, unmanned vehicles such as UAVs can move not only on a two-dimensional plane. It is necessary to propose methods to estimate three-dimensional rotation. Vanishing points in the environment would be the effective information for estimating the rotation of robots or unmanned vehicles. And in this thesis, the methods are proposed to solve the vanishing points extraction for omnidirectional images taken by an omnidirectional camera and to estimate rotation of the omnidirectional camera.
For vanishing points extraction, we use edge detection for omnidirectional images to find out the boundary points and then link these points as line segments. After line detection and vanishing points extraction for these line segments, the directions information of vanishing points in the surroundings can be obtained and be applied for rotation estimation later. For camera calibration, we propose a method using lines in omnidirectional images to obtain camera intrinsic parameters, and the camera intrinsic parameters will be applied for line detection and vanishing points extraction later. Vanishing points extraction for omnidirectional images taken by the omnidirectional camera in the indoor environment would be applied to estimate the camera’s rotation. The results will be shown and compared with the results calculated by the method using vertical lines tracking. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T13:43:15Z (GMT). No. of bitstreams: 1 ntu-104-R02921067-1.pdf: 7151281 bytes, checksum: 9da91eed1974ed9057092545684fa40f (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 摘要................................................................................................................................. i
ABSTRACT ................................................................................................................... ii Contents ........................................................................................................................ iv List of Figures ............................................................................................................... vi List of Tables ................................................................................................................. ix Chapter 1 Introduction ................................................................................................... 1 1.1 Motivation ......................................................................................................... 1 1.2 Problem Formulation ........................................................................................ 2 1.3 Contribution of the Thesis ................................................................................ 5 1.4 Organization of the Thesis ................................................................................ 6 Chapter 2 Literature Survey ........................................................................................... 7 2.1 Omnidirectional Vision-Based Localization ..................................................... 7 2.2 Omnidirectional Vision-Based Vanishing Points Extraction .......................... 10 2.3 Omnidirectional Camera Calibration .............................................................. 13 Chapter 3 Related Algorithms ...................................................................................... 15 3.1 Homography ................................................................................................... 15 3.2 Cam Space and World Space .......................................................................... 17 3.3 Vanishing Points Extraction ............................................................................ 22 3.3.1 Line Detection .................................................................................... 22 3.3.2 Splitting Criteria ................................................................................. 23 3.3.3 Merging Criteria ................................................................................. 25 3.3.4 Vanishing Points Extraction ............................................................... 28 Chapter 4 Analysis of Vanishing Points ....................................................................... 32 4.1 Vanishing Points Extrication for Omnidirectional Images ............................. 32 4.1.1 System Architecture ........................................................................... 32 4.1.2 Edge Linking ...................................................................................... 33 4.1.3 Line Detection .................................................................................... 38 4.1.4 Vanishing Points Extraction ............................................................... 40 4.1.5 Rotation Calculation ........................................................................... 44 4.2 Camera Calibration using Lines in Images ..................................................... 52 4.2.1 Find f′(r) ........................................................................................... 52 4.2.2 f′(r) versus f(r) ............................................................................... 53 4.2.3 Different Centers ................................................................................ 54 4.2.4 Fine the Center ................................................................................... 58 4.2.5 Find a0 and 푓 (r) ............................................................................. 59 Chapter 5 Analysis of Vanishing Points in Multi-scene Experiment ........................... 60 5.1 Camera Calibration ......................................................................................... 60 5.1.1 Camera Calibration using Chessboard Patterns ................................. 60 5.1.2 Camera Calibration using Lines in Images ........................................ 62 5.2 Vertical Lines Tracking ................................................................................... 66 5.2.1 Pure Rotation ...................................................................................... 71 5.2.2 Pure Translation .................................................................................. 73 5.2.3 Vertical Lines Matching ..................................................................... 75 5.3. Comparative Analysis of Camera Rotation ................................................... 78 5.3.1 The First Experimental ....................................................................... 78 5.3.2 Vanishing Points Extraction ............................................................... 82 5.3.3 Rotation Estimation ............................................................................ 90 5.3.4 The Second Experiment ..................................................................... 97 5.3.5 The Third Experiment ...................................................................... 101 Chapter 6 Conclusions and Future Works .................................................................. 106 6.1 Conclusions ................................................................................................... 106 6.2 Future Works ................................................................................................. 107 References .................................................................................................................. 108 | |
| dc.language.iso | en | |
| dc.subject | 全向影像 | zh_TW |
| dc.subject | 全向相機 | zh_TW |
| dc.subject | 旋轉估測 | zh_TW |
| dc.subject | 消失點提取 | zh_TW |
| dc.subject | 相機校正 | zh_TW |
| dc.subject | rotation estimation | en |
| dc.subject | vanishing points extraction | en |
| dc.subject | omnidirectional image | en |
| dc.subject | omnidirectional camera | en |
| dc.subject | camera calibration | en |
| dc.title | 利用全向影像偵測室內環境消失點與估測旋轉量 | zh_TW |
| dc.title | Rotation Estimation and Vanishing Point Extraction for Indoor Omnidirectional Images | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 簡忠漢(Jong-Hann Jean),李後燦(Hou-Tsan Lee) | |
| dc.subject.keyword | 全向相機,全向影像,消失點提取,旋轉估測,相機校正, | zh_TW |
| dc.subject.keyword | omnidirectional camera,omnidirectional image,vanishing points extraction,rotation estimation,camera calibration, | en |
| dc.relation.page | 116 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-12-22 | |
| 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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