時至今日，自動駕駛技術已越來越受重視，自動駕駛車輛可協助紓緩交通且能降低人為疏失所造成的交通事故之發生。在自動駕駛當中，精確車輛定位導航為不可或缺之技術。要完成精確車輛定位，其中一種方法為利用事先建立之精密地圖，當車輛行駛時，利用相機和雷射掃描之結果與地圖進行匹配，判斷車輛當前所在之位置。然而，精密地圖之建立需耗費眾多人力與時間且系統需要大量儲存空間來存放地圖。若要降低系統對精密地圖之依賴，即時動態定位技術(Real Time Kinematic, RTK)為一有效之方法。但衛星定位系統之精確度會受到多路徑效應與訊號遮蔽之影響而降低，因此系統需能偵測出不正常之定位解並利用不同之里程計協助車輛於無衛星訊號的環境下進行定位。除此之外，所有感測器皆有可能產生不正常之結果，系統需能判斷當下能夠信賴之感測器為何。
本論文研究RTK定位技術為基礎之自動駕駛系統並利用不同感測器之里程計方法如視覺里程計、雷射點雲匹配與慣性導航協助系統於衛星訊號受影響之區域定位。為探討系統之完整性，本論文亦探討各感測器於何種環境中會產生不正常之導航解，並利用實際收取之資料進行驗證。

Autonomous vehicle is getting more and more attention today. With autonomous vehicle, people get more free time during driving. Traffic jams can be reduced and traffic accidents caused by human errors may be lower. A key technology for an autonomous car is the ability to localize itself accurately. For accurate positioning, one method is to localize the car by prebuilt maps. Navigation systems match the measurements from external sensors like laser scanners or cameras to the map and determine the position of the vehicle. However, building dense maps is time consuming and complex and systems need a mass storage device for the maps. To ease the relying on dense maps, real time kinematic satellite navigation system is a way to fulfill the requirement but the performance of satellite navigation systems may degrade due to multipath effect or signal attenuation. Hence, navigation systems must diagnose the abnormal GPS results and employ different odometry techniques for the navigation tasks under GPS-denied environments. Besides, all sensors are subject to errors. It is critical for systems to decide which and when the sensor measurements are reliable.
The thesis studies the use of RTK-GPS based navigation system to navigate the autonomous car without prebuilt dense maps. Real world experiments show the capability of the system to navigate the driverless car successfully under normal condition. To account the navigation tasks under GPS-denied environments, odometry techniques which can be used to back up the degradation or loss of the RTK-GPS signal are investigated. Different odometry techniques include point cloud scan matching, visual odometry and inertial navigation systems are described. For sensor integrity, scenarios lead to performance degradation of odometry techniques are investigated and analysis of the uncertainty of the odometry techniques is studied. Finally, real world experiments are carried out to evaluate the sensor fusion navigation system.

[1] M. Leingartner, J. Maurer, A. Ferrein, and G. Steinbauer, "Evaluation of Sensors and Mapping Approaches for Disasters in Tunnels," Journal of Field Robotics, 2015.
[2] D. Ball, B. Upcroft, G. Wyeth, P. Corke, A. English, P. Ross, et al., "Vision‐based Obstacle Detection and Navigation for an Agricultural Robot," Journal of Field Robotics, 2016.
[3] J. S. Levinson, S. Thrun, D. Koller, and M. Levoy, Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles: Stanford University, 2011.
[4] J. Ziegler, P. Bender, M. Schreiber, H. Lategahn, T. Strauss, C. Stiller, et al., "Making Bertha Drive—an Autonomous Journey on a Historic Route," IEEE, Intelligent Transportation Systems Magazine, vol. 6, pp. 8-20, 2014.
[5] J. M. Webster, "A Localization Solution for an Autonomous Vehicle in an Urban Environment," 2007.
[6] S. Scheding, T. Peynot, and S. Terho, "Sensor Data Integrity," DTIC Document2008.
[7] E. Abbott and D. Powell, "Land-vehicle Navigation Using GPS,", vol. 87, pp. 145-162, 1999.
[8] A. Noureldin, T. B. Karamat, and J. Georgy, Fundamentals of Inertial Navigation, Satellite-based Positioning and Their Integration: Springer Science & Business Media, 2012.
[9] P. D. Groves, Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems: Artech house, 2013.
[10] A. Bacha, C. Bauman, R. Faruque, M. Fleming, C. Terwelp, C. Reinholtz, et al., "Odin: Team victor Tango's Entry in the DARPA Urban Challenge," Journal of Field Robotics, vol. 25, pp. 467-492, 2008.
[11] B. J. Patz, Y. Papelis, R. Pillat, G. Stein, and D. Harper, "A Practical Approach to Robotic Design for the Darpa Urban Challenge," Journal of Field Robotics, vol. 25, pp. 528-566, 2008.
[12] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics: MIT press, 2005.
[13] A. Nüchter, 3D Robotic Mapping: the Simultaneous Localization and Mapping Problem with Six Degrees of Freedom vol. 52: Springer, 2008.
[14] E. B. Olson, "Real-time Correlative Scan Matching," in IEEE International Conference on Robotics and Automation, 2009.
[15] G. Grisetti, C. Stachniss, and W. Burgard, "Improved Techniques for Grid Mapping with Rao-blackwellized Particle Filters," IEEE transactions on Robotics, vol. 23, pp. 34-46, 2007.
[16] J. Civera, A. J. Davison, and J. M. M. Montiel, Structure from Motion Using the Extended Kalman Filter vol. 75: Springer Science & Business Media, 2011.
[17] D. Scaramuzza and F. Fraundorfer, "Visual Odometry [tutorial]," IEEE, Robotics & Automation Magazine, vol. 18, pp. 80-92, 2011.
[18] F. Fraundorfer and D. Scaramuzza, "Visual Odometry: Part II: Matching, Robustness, Optimization, and Applications," IEEE, Robotics & Automation Magazine, vol. 19, pp. 78-90, 2012.
[19] M. Aeberhard, S. Rauch, M. Bahram, G. Tanzmeister, J. Thomas, Y. Pilat, et al., "Experience, Results and Lessons Learned from Automated Driving on Germany's Highways," IEEE Intelligent Transportation Systems Magazine, vol. 7, pp. 42-57, 2015.
[20] I. Skog and P. Händel, "In-car Positioning and Navigation Technologies—A Survey," IEEE Transactions on Intelligent Transportation Systems, vol. 10, pp. 4-21, 2009.
[21] D. Titterton and J. L. Weston, Strapdown Inertial Navigation Technology vol. 17: IET, 2004.
[22] D. Nistér, O. Naroditsky, and J. Bergen, "Visual Odometry," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 652-659, 2004.
[23] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision: Cambridge university press, 2003.
[24] A. Geiger, J. Ziegler, and C. Stiller, "Stereoscan: Dense 3d Reconstruction in Real-time," in IEEE, Intelligent Vehicles Symposium (IV), pp. 963-968, 2011.
[25] C. Harris and M. Stephens, "A Combined Corner and Edge Detector," in Alvey vision conference, p. 50, 1988.
[26] S. Baker and I. Matthews, "Lucas-kanade 20 Years on: A Unifying Framework," International Journal of Computer Vision, vol. 56, pp. 221-255, 2004.
[27] Z. Kalal, K. Mikolajczyk, and J. Matas, "Forward-backward Error: Automatic Detection of Tracking Failures," in 20th International Conference on Pattern Recognition (ICPR), pp. 2756-2759, 2010.
[28] P. J. Besl and N. D. McKay, "Method for Registration of 3-D Shapes," in Robotics-DL tentative, pp. 586-606, 1992.
[29] S. Rusinkiewicz and M. Levoy, "Efficient Variants of the ICP Algorithm," in Third International Conference on 3-D Digital Imaging and Modeling, pp. 145-152, 2001.
[30] O. Bengtsson and A.-J. Baerveldt, "Robot Localization based on Scan-matching—Estimating the Covariance Matrix for the IDC algorithm," Robotics and Autonomous Systems, vol. 44, pp. 29-40, 2003.
[31] A. Censi, "An ICP Variant Using a Point-to-line Metric," in IEEE International Conference on Robotics and Automation, pp. 19-25, 2008.
[32] A. Segal, D. Haehnel, and S. Thrun, "Generalized-ICP," in Robotics: Science and Systems, 2009.
[33] F. Pomerleau, F. Colas, and R. Siegwart, "A Review of Point Cloud Registration Algorithms for Mobile Robotics," Foundations and Trends in Robotics (FnTROB), vol. 4, pp. 1--104, 2015.
[34] Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software: John Wiley & Sons, 2004.
[35] R. Kümmerle, B. Steder, C. Dornhege, M. Ruhnke, G. Grisetti, C. Stachniss, et al., "On Measuring the Accuracy of SLAM Algorithms," Autonomous Robots, vol. 27, pp. 387-407, 2009.
[36] T. Papadopoulo and M. I. Lourakis, "Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications," in European Conference on Computer Vision, pp. 554-570, 2000.
[37] 李堃瑞, "應用粒子濾波器追蹤演算法於自主車輛駕駛之研究," 2013.
[38] R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," Journal of basic Engineering, vol. 82, pp. 35-45, 1960.