對自動駕駛車輛而言，精準的定位是最基本和最重要的任務，許多的感測器和定位技術應運而生，例如：全球衛星導航系統(GNSS)、光達(LiDAR)、慣性導航系統(INS)…等，這些技術在空曠平坦的環境中可以達到公分等級的定位效果，並能符合自動駕駛對定位精準度的要求。然而高爾夫球場是一個地形起伏劇烈且四周被樹木環繞的環境，崎嶇地形對慣性導航系統和基於光達的地圖匹配(Map-Matching)定位演算法是很大的障礙，而四周環繞的樹木容易遮蔽和反射衛星訊號對造成全球衛星定位系統造成很大的負面影響，因此應用在高爾夫球場自動駕駛的定位系統成為一個重要且充滿挑戰的研究主題。
本文提出了一個融合了 GNSS、UWB 和 IMU 的協同導航系統來滿足在高爾夫球場執行自動駕駛的定位需求。UWB 是一種具有很好的抗多路徑效應的通訊技術且價格不高，因此，考量到成本及高爾夫球場的多路徑效應影響，本文不採用高昂成本的即時定位技術(RTK)或是光達，而是使用消費者等級的 GNSS 接收機、UWB 感測器和微機電系統(MEMS)的 IMU，在擴展卡爾曼濾波器(EKF)的架構下，本論文所提出的方法有效地融合三個感測器的定位結果，透過多種感測器資料各自的優勢彌補彼此的不足之處，此外本論文還針對環境的不確定性對感測器量測的影響即時的調整測量雜訊矩陣(measurement noise covariance)。
本論文提出的導航系統成功地運用在了國立成功大學自動駕駛高爾夫球車當中，並且在台灣桃園揚昇高爾夫球俱樂部進行實際的測試和評估其定位性能。

For autonomous vehicles, precise localization is a crucial task. Various sensors and localization technologies have emerged, such as Global Navigation Satellite Systems (GNSS), Light Detection and Ranging (LiDAR), and Inertial Navigation Systems (INS) to provide position and velocity information. These technologies can achieve centimeter-level localization accuracy in open and flat environments, meeting the requirements of autonomous driving. However, golf courses have rugged terrains, posing significant challenges for INS and map-matching algorithms based on LiDAR. Also, the surrounding trees can easily block and reflect satellite signals, causing errors on GNSS positioning. Therefore, developing a navigation system for autonomous driving on golf courses becomes an important and challenging research topic.
This thesis proposes a collaborative navigation system using GNSS, Ultra-Wideband (UWB), and Inertial Measurement Units (IMU) to meet the localization requirements for autonomous driving on golf courses. UWB is a communication technology with excellent multipath resistance and low cost. Considering the cost and multipath effects on golf courses, this thesis does not employ expensive real-time localization techniques like Real-Time Kinematic (RTK) or LiDAR. Instead, it uses consumer-grade GNSS receivers, UWB sensors, and Microelectromechanical Systems (MEMS) IMUs. Within the framework of the Extended Kalman Filter (EKF), the proposed method effectively fuses the localization results from the three sensors. The aim is to compensate for each sensor's limitations by leveraging the advantages of multiple sensor data. Additionally, this thesis dynamically adjusts the Measurement Noise Covariance (MNC) in real-time to account for environmental uncertainties' impact on sensor measurements.
The proposed navigation system has been successfully applied in National Cheng Kung University (NCKU) autonomous golf car, and its localization performance has been tested and evaluated at the Sunrise Golf and Country Club in Taoyuan, Taiwan.

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