在過去的數十年內，視覺里程計迅速發展並在定位技術上獲得許多關注與討論。視覺里程計技術是透過連續影像像對中的豐富內容，實現相機或載台的定位。最著名的視覺里程計應用是機器人於火星的探勘任務。視覺里程計能夠依照使用的方法來區分種類。在所有方法中，基於影像特徵視覺里程計的方法最廣為使用。然而此種方法會在特徵豐富的場景下，遭遇到運算上的困難。因此，本研究將嘗試使用影像像對間的相對方位來取代傳統像點作為觀測量。
隨著電腦視覺領域的快速發展，攝影測量結合電腦視覺之應用也逐漸崛起。更多的研究融合電腦視覺領域與攝影測量來解決多樣的問題。其中，影像的內方位及外方位參數在攝影測量領域中備受重視。然而這些參數的單位以及表示法在此兩個領域中有所不同，造成許多混淆與融合上的不便。為求得可靠的觀測結果，內、外方位參數在兩個領域中都極其重要。在攝影測量領域中，旋轉角的表示法通常為歐拉角。但是，歐拉角仍有12種表示法，在使用上會遭遇模稜兩可的情形。歐拉角也會在碰到萬向鎖問題時，失去一個旋轉上的自由度。模稜兩可的情況與萬向鎖問題在領域融合時造成其許多使用上的不便利性。因此，四元素將在此研究中替代歐拉角。
雙相機視覺里程計是利用已知且固定之相對方位的雙相機所拍攝的立體相對來重建相機或載台之移動位置與姿態。由於雙相機所拍攝之立體相對能解決單相機視覺里程計中尺度不確定性與影像幾何不穩定性，此研究將基於雙相機視覺里程計。研究方法可分為兩個階段。第一階段為獲取像對間的相對方位觀測量，第二階段則是將解算出來的姿態與位置進行平差與最佳化來獲得相機或載台的移動軌跡。影像像對間的相對方位是由影像匹配後所解算出的本質矩陣分解而來的。在獲得可靠的相對方位觀測量後，會使用條件網形平差來最佳化相機/載台之姿態。
為了測試基於四元素之相對方位網形平差的可行性，本研究以室內場域與KITTI資料集影像進行兩個實驗。實驗結果顯示基於四元素之方法與基於旋轉矩陣之方法有相似的精度並且運算效率達到基於旋轉矩陣之方法的兩倍左右。在兩種方法中都解算出合理的漂移比。

Over the past few decades, visual odometry (VO) developed rapidly and gained much attention in the field of localization technique. By the rich information included in sequential images, the localization of the camera or the vehicle it is mounted to could be performed. The most well-known VO application is the Mars exploration rovers. VO could be divided into different approaches. Among all the approaches, the feature-based approach is the most popular. However, the featured-based VO approach might face great computation challenge when encountering scenarios with rich texture that results in large number of observations. Therefore, the relative orientation is expected to replace the image points for observations in this study.
Moreover, with the rapid development in computer vision field, photogrammetric computer vision also blooms. More and more research fuses computer vision field with photogrammetry field to solve and deal with various types of problem. The intrinsic and extrinsic parameters of camera/image are vital in the photogrammetry field. However, the representations and units of these parameters differs from photogrammetry and computer vision convention and software, making the representation of intrinsic and extrinsic parameters of the camera confusing. Intrinsic and extrinsic parameters of the camera/image are needed to obtain reliable results in both field of studies. In photogrammetry field, Euler angle is usually chosen as the representation for rotation. However, Euler angle causes some ambiguity and problem. Moreover, Euler angle may also result in losing one axis of rotation when encountering gimbal lock problem. The ambiguity and gimbal lock problem with Euler angle make it inconvenient for users to deal with when fusing both fields. Consequently, the quaternion is an alternative to represent rotation in this study
Stereo Visual Odometry is the method of reconstructing the movement of position and attitude by using a stereo camera with known and fixed relative orientation to obtain stereo pair images at the same time. It could help deal with the scale and geometry problem that exist in monocular visual odometry and is therefore applied in this study. The research approach could be separate into two stages. The first stage is the relative orientations observation acquisition between image pairs. Followed by the second stage which is the local optimization of the estimated orientation to recover the trajectory of camera/vehicle. The relative orientations between image pairs are being estimated by the essential matrix solution after image matching process. After obtaining reliable relative orientation observations, the network adjustments with the constraints in between the stereo camera will be conducted to solve the optimal solutions for the camera/vehicle orientation.
To test the feasibility of network adjustment of relative orientation based on quaternion, experiments on an indoor test field and the KITTI dataset are being conducted. Results show that the precision of quaternion-based method has about the same quality as rotation matrix-based method and that the computation time of quaternion-based method is about twice faster than rotation matrix-based method. Results in both experiments show reasonable drift ratios.

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

Cumani A., 2011, “Feature localization refinement for improved visual odometry accuracy”. International Journal of Circuits, Systems and Signal Processing 5(2):151–158

Davison A., 2003, “Real-time simultaneous localisation and mapping with a single camera”, in Proc. Int. Conf. Computer Vision, pp. 1403– 1410.

Fischler, M. A. and R.C. Bolles, 1981, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”. Communications of the ACM, 24(6), 381-395.

Fraundorfer F. and D. Scaramuzza, 2012, “Visual odometry: Part II: matching, robustness, optimization, and applications”, IEEE Robot Autom Mag 19(2):78–90.

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

Gonzalez, R., F. Rodriguez, J.L. Guzman, C. Pradalier and R. Siegwart, 2012, “Combined visual odometry and visual compass for off-road mobile robots localization”. Robotica, 30(6), 865-878.

Geiger, A., P. Lenz, C. Stiller and R. Urtasun, 2013, “Vision meets robotics: The kitti dataset”. The International Journal of Robotics Research, 32(11), 1231-1237.

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

Helmick D.M., Y. Cheng, D.S. Clouse, L.H. Matthies and S.I. Roumeliotis, 2004, “Path following using visual odometry for a mars rover in high-slip environments”. In: Proceedings 2004 anonymous aerospace conference on IEEE, 2004. vol 2, IEEE, Piscataway, p 772–789.

Johnson, A. E., S.B. Goldberg, Y. Cheng and L.H. Matthies, 2008, May, “Robust and efficient stereo feature tracking for visual odometry”. In 2008 IEEE international conference on robotics and automation (pp. 39-46). IEEE.

Kitt B.M., J. Rehder, A.D. Chambers, M. Schonbein, H. Lategahn and S. Singh, 2011, “Monocular visual odometry using a planar road model to solve scale ambiguity”. In: Proceedings of the European conference on mobile robots, Örebro University, Sweden, p 43–48

Kicman P. and J. Narkiewicz, 2013, “Concept of integrated INS/visual system for autonomous mobile robot operation”. Marine navigation and safety of sea transportation: navigational problems, CRC Press, p 35–40. ISBN: 978-1-138-00107-7

Kupfer, B., N.S. Netanyahu and I. Shimshoni, 2014, “An efficient SIFT-based mode-seeking algorithm for sub-pixel registration of remotely sensed images”. IEEE Geoscience and Remote Sensing Letters, 12(2), 379-383.

Lin, K. Y., Tseng, Y. H., & Chiang, K. W, 2020, “Network Adjustment of Automated Relative Orientation for a Dual-Camera System”. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 49-54.

Longuet-Higgins, H.C., 1981, “A computer algorithm for reconstructing a scene from two projections”. Nature, vol. 293, no. 10, pp. 133–135.

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

Ma, W., Z. Wen, Y. Wu, L. Jiao, M. Gong, Y. Zheng and L. Liu, 2016, “Remote sensing image registration with modified SIFT and enhanced feature matching”. IEEE Geoscience and Remote Sensing Letters, 14(1), 3-7.

Maimone M, Y. Cheng and L.H. Matthies, 2007, “Two years of visual odometry on the mars exploration rovers”. Journal of Field Robotics 24(3):169–186

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

Nistér D., O. Naroditsky and J. Bergen, 2006, “Visual odometry for ground vehicle applications”. Journal of Field Robotics 23(1):3–20

Nourani‐Vatani, N. and P. V. K. Borges, 2011, “Correlation‐based visual odometry for ground vehicles”. Journal of Field Robotics, 28(5), 742-768.

Rublee, E., V. Rabaud, K. Konolige and G. Bradski, 2011, November, “ORB: An efficient alternative to SIFT or SURF”. In 2011 International conference on computer vision (pp. 2564-2571). IEEE.

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.

Torr, P. H. and A. Zisserman, 2000, “MLESAC: A new robust estimator with application to estimating image geometry”. Computer vision and image understanding, 78(1), 138-156.