進階搜尋


下載電子全文  
系統識別號 U0026-0108201814574500
論文名稱(中文) 以解算連續像對相對方位參數之視覺里程計
論文名稱(英文) Visual Odometry by Solving Relative Orientation Parameters of Consecutive Image Pairs
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 106
學期 2
出版年 107
研究生(中文) 林照捷
研究生(英文) Chao-Chieh Lin
學號 P66054094
學位類別 碩士
語文別 英文
論文頁數 109頁
口試委員 指導教授-曾義星
口試委員-史天元
口試委員-趙鍵哲
口試委員-張智安
中文關鍵字 單眼視覺里程計  相對方位  視覺里程計中的累積誤差 
英文關鍵字 Monocular Visual Odometry  Relative Orientation  Accumulated Errors in Visual Odometry 
學科別分類
中文摘要 過去數十年來,視覺里程計在導航技術領域中被廣泛的討論以及大規模的發展。視覺里程計技術是透過分析連續像對讓機器人可以在一個陌生的環境中持續且自動的定位,過程中不需要人為的輸入及互動,其中最有名的例子就是在2003年的機器人火星探勘任務。這項技術雖然蓬勃於電腦視覺,但是其實傳統的攝影測量也有類似的定位技術,也就是我們熟悉的恢復相機外方位參數以及製圖。但由於過程所使用的方法不同,導致這兩個領域的技術最後有著不同的應用,其中視覺里程計強調即時定位導航,而攝影測量則是致力於高精度的測繪製圖。因此,本研究希望結合兩種不同方法的優點來探討如何使用單眼相機的拍攝來恢復當時的拍攝路徑,藉以達到導航的概念。

本研究可分為兩階段,分別是探討一組立體像對的相對方位參數以及多組連續立體像對的連續外方位參數。在本研究中,求解一組立體像對參數是相對重要且基礎的工作,因此在第一個部分主要針對一組像對提出一個合適的匹配策略以及自動求解相對方位的方法,來求算可靠且穩定的相對方位。雖然電腦視覺中解算相對方位相當快速且自動化,但是衍生出的兩種投影模糊性是必須解決的,其中包含了決定四組外方位參數的不確定性,以及恢復相對平移的真實尺度。有了可靠的相對方位後,可以從第一張相片開始,將每組相對方位疊加,所求得的整體相對方位被稱為連續相對方位。在疊加的過程中,每組相對方位的誤差將會持續累積,導致相機路徑會隨著時間飄移。因此,本研究提出一個針對相對方位的網型平差法來解決導航過程中的誤差累積問題。

為了證明單眼視覺里程計以及相對方位網型平差之可行性,本研究分別設計了一個室外及室內實驗。室外實驗是在成功大學測量系館外朝著系館連續拍攝4張影像(3個高重疊連續像對),而室內實驗是在成功大學測量系館內沿著樓梯並且朝著地面以及牆面連續拍攝10張影像(9個高重疊連續像對)。兩個實驗皆結果顯示使用本研究所提出之單眼視覺里程方法可以重建出合理的相機路徑以及周圍環境的稀疏物點,而且多數特徵都可以從物點圖中清楚地被辨識出來。另一方面,第一個實驗結果顯示透過比較網型平差前後的相機路徑以及實際相機路徑,可以發現導航中的誤差累積現象可以有效的被降低。而且,兩個實驗結果也顯示經過網型平差後,來自不同像對的物點的偏差量皆明顯的下降。根據以上兩個實驗成果,可以證實使用針對相對方位的網型平差確實可以有效的減少累積誤差對導航的影響。

本研究證實使用來自電腦視覺以及攝影測量之綜合方法於單眼視覺里程之可行性。研究中所提出的針對相對方位之網型平差可以有效減少導航中的累積誤差問題,而且透過相對方位之網型平差可以解決單眼視覺里程中的尺度不確定性問題。
英文摘要 In the past few decades, Visual Odometry (VO) in computer vision has been widely developed and discussed in positioning and navigation technique. Through analyzing consecutive image pairs, the robotic can localize itself autonomously and continuously in an unknown environment without any human input. The most famous example is robotic space mission on Mars in 2003. Actually, photogrammetry can also achieve localization of the platform, and it is known as recovering exterior orientation parameters (EOPs) of camera. Because of the different methodology exploited in these two fields, VO focuses on real time navigation and photogrammetry aims to the off-line mapping. Consequently, combinable methods containing VO in computer vision and photogrammetry are used to reconstruct the camera path in an unknown environment in this study.

This study can be divided into two parts: Relative Orientation Parameters (ROPs) of an image pair and Coherent Relative Orientation Parameters (CROPs) of consecutive images pairs. Firstly, solving ROPs of an image pair is a critical and fundamental work in VO technique, so an appropriate matching strategy and an automatic ROPs method are applied in this study to solve reliable ROPs of an image pair. Though automatic method is convenient, the projective ambiguity containing scale and four-fold ambiguity should be solved in this study. Secondly, CROPs can be simply described as the overall EOPs of consecutive images. Since the localization of consecutive images is incremental, the errors are accumulated over time. In this study, a preliminary study of network adjustment of ROPs is proposed to suppress the accumulated error in the navigation.

For validation of feasibility of proposed monocular VO and network adjustment of ROPs, two experiments including indoor and outdoor tests are conducted in this study. In outdoor test, 3 consecutive images pairs are captured in outdoor environment of Department of Geomatics. In indoor test, 9 consecutive images pairs are captured up the stairs in Department of Geomatics. In analysis of monocular VO, the results show that the estimated camera path and reconstructed 3D object points are reasonable, and the features in reconstruction can be recognized clearly. In analysis of network adjustment, comparing estimated camera paths with actual camera path, the accumulated errors reduce significantly in outdoor test when employing network adjustment. Furthermore, the deviation of object points derived from different image pairs are decreased in both two tests. Consequently, the network adjustment in this study is effective to reduce the accumulated errors of the camera path.

This study shows the feasibility of monocular VO with combinable methods containing photogrammetry and VO in computer vision. The proposed network adjustments of ROPs for accumulated errors in navigation is validated. In addition, real scale of translation in monocular VO can also be solved in network adjustments of ROPs in this study.
論文目次 摘要 I
ABSTRACT III
ACKNOWLEDGEMENT V
CONTENTS VI
LIST OF TABLES VIII
LIST OF FIGURES IX
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Definition and Objective 6
1.3 Research Approach 10
1.4 Thesis Structure 12
Chapter 2 Image Matching 13
2.1 Feature Detection, Extraction and Matching 13
2.2 Random Sample Consensus (RANSAC) 18
Chapter 3 Relative Orientation 26
3.1 Geometry of Frame Camera 26
3.2 Geometry of Relative Orientation 39
3.3 Essential Matrix 42
3.4 Recovering ROPs from EM 46
Chapter 4 Coherent Relative Orientation 49
4.1 Consecutive Image Pairs 49
4.2 Sequential Method 52
4.3 Network Image Pairs 55
4.4 Network adjustment of ROPs 61
Chapter 5 Experiments 68
5.1 Test Data Preparation 68
5.1.1 Camera Calibration 68
5.1.2 Outdoor Test Field 69
5.1.3 Indoor Test Field 71
5.2 Test Result and discussions of Outdoor Experiment
75
5.3 Test Result and Discussions of Indoor Experiment
84
Chapter 6 Conclusions and Suggestions 94
6.1 Conclusions 94
6.2 Suggestions 97
REFERENCES 98
APPENDIX 103
參考文獻 Agrawal, M. and K. Konolige, 2006, August, “Real-time localization in outdoor environments using stereo vision and inexpensive GPS”, In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on (Vol. 3, pp. 1063-1068). IEEE.

Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool, 2008, “Speeded-up robust features (SURF)”, Computer vision and image understanding, 110(3), pp.346-359.

Borenstein, J. and L. Feng, 1996, “Measurement and correction of systematic odometry errors in mobile robots”, IEEE Transactions on robotics and automation, 12(6), pp.869-880.

Chen, H.R., 2016, “Automatic image matching and georeferencing of digitized historical aerial photographs”, Master’s Thesis, Department of Geomatics, National Cheng Kung University.

Cheng, Y., M. Maimone and L. Matthies, 2005, “October. Visual odometry on the Mars exploration rovers”, In Systems, Man and Cybernetics, 2005 IEEE International Conference on(Vol. 1, pp. 903-910). IEEE.

Davison, A.J., 2003, “October. Real-time simultaneous localisation and mapping with a single camera”, In null (p. 1403). IEEE.

De Berg, M., M. Van Kreveld, M. Overmars and O.C. Schwarzkopf, 2000, “Computational geometry”, In Computational geometry (pp. 1-17). Springer, Berlin, Heidelberg.


Engel, J., J. Sturm, and D. Cremers, 2007, October. “Camera-based navigation of a low-cost quadrocopter.” In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on (pp. 2815-2821). IEEE.

Fitzgibbon, A.W., 2001, “Simultaneous linear estimation of multiple view geometry and lens distortion”, In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on (Vol. 1, pp. I-I). IEEE.

Forster, C., M. Pizzoli, and D. Scaramuzza, 2014, May, “SVO: Fast semi-direct monocular visual odometry”, In Robotics and Automation (ICRA), 2014 IEEE International Conference on(pp. 15-22). IEEE.

Fraundorfer, F. and D. Scaramuzza, 2012. “Visual odometry: Part ii: Matching, robustness, optimization, and applications”, IEEE Robotics & Automation Magazine, 19(2), pp.78-90.

Fuentes-Pacheco, J., J. Ruiz-Ascencio and J.M. Rendón-Mancha, 2015. “Visual simultaneous localization and mapping: a survey”, Artificial Intelligence Review, 43(1), pp.55-81.

Fischler, M.A. and R.C. Bolles, 1987, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”, In Readings in computer vision (pp. 726-740).

Fularz, M., M. Kraft, A. Schmidt and A. Kasiński, 2012, “FPGA implementation of the robust essential matrix estimation with RANSAC and the 8-point and the 5-point method”, In Facing the Multicore-Challenge II (pp. 60-71). Springer Berlin Heidelberg.

Förstner, W., 2002, “Computer vision and photogrammetry–mutual questions: geometry, statistics and cognition”, Bildteknik/lmage Science, Swedish Society for Photogrammetry and Remote Sensing, pp.151-164.

Grisetti, G., R. Kummerle, C. Stachniss and W. Burgard, 2010. “A tutorial on graph-based SLAM”, IEEE Intelligent Transportation Systems Magazine, 2(4), pp.31-43.

Howard, A., 2008, September, “Real-time stereo visual odometry for autonomous ground vehicles”, In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on (pp. 3946-3952). IEEE.

Häne, C., T. Sattler and M. Pollefeys, 2015, September, “Obstacle detection for self-driving cars using only monocular cameras and wheel odometry”, In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on (pp. 5101-5108). IEEE.

Helmick, D.M., Y. Cheng, D.S. Clouse, L.H. Matthies and S.I. Roumeliotis, 2004, March, “Path following using visual odometry for a mars rover in high-slip environments”, In Aerospace Conference, 2004. Proceedings. 2004 IEEE (Vol. 2, pp. 772-789). IEEE.

Hartley, R. and A. Zisserman, 2003, “Multiple view geometry in computer vision”, Cambridge university press.

Hartley, R.I., 1997, “In defense of the eight-point algorithm”, IEEE Transactions on pattern analysis and machine intelligence, 19(6), pp.580-593.

Joglekar, J. and S.S. Gedam, 2012, “Area based image matching methods—A survey”, Int. J. Emerg. Technol. Adv. Eng, 2(1), pp.130-136.

Juan, L. and O. Gwun, 2009, “A comparison of sift, pca-sift and surf”, International Journal of Image Processing (IJIP), 3(4), pp.143-152.

Lowe, D.G., 2004, “Distinctive image features from scale-invariant keypoints”, International journal of computer vision, 60(2), pp.91-110.

Longuet-Higgins, H.C., 1981, September, “A computer algorithm for reconstructing a scene from two projections”, Nature. 293 (5828): 133–135.
Levi, R.W. and T. Judd, 1996, “Dead reckoning navigational system using accelerometer to measure foot impacts”, U.S. Patent 5,583,776.

Lemaire, T., C. Berger, I.K. Jung and S. Lacroix, 2007, “Vision-based slam: Stereo and monocular approaches”, International Journal of Computer Vision, 74(3), pp.343-364.

Nistér, D., O. Naroditsky and J. Bergen, 2004, June. “Visual odometry”, In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on (Vol. 1, pp. I-I). Ieee.

Nistér, D., 2004, “An efficient solution to the five-point relative pose problem”, IEEE transactions on pattern analysis and machine intelligence, 26(6), pp.756-770.

Scaramuzza, D. and F. Fraundorfer, 2011, “Visual odometry [tutorial]”, IEEE robotics & automation magazine, 18(4), pp.80-92.

Triggs, B., P.F. McLauchlan, R.I. Hartley and A.W. Fitzgibbon, 1999, September. “Bundle adjustment—a modern synthesis”, In International workshop on vision algorithms (pp. 298-372). Springer, Berlin, Heidelberg.

Williams, B., M. Cummins, J. Neira, P. Newman, I. Reid, and J. Tardós, 2009, “A comparison of loop closing techniques in monocular SLAM”, Robotics and Autonomous Systems, 57(12), pp.1188-1197.

Zhou, D., Y. Dai and H. Li, 2016, June, “Reliable scale estimation and correction for monocular visual odometry”, In Intelligent Vehicles Symposium (IV), 2016 IEEE (pp. 490-495). IEEE.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2018-08-10起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2018-08-10起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw