高精度定位是自動駕駛最基本也是最重要的課題。隨著技術的進步，越來越多的感測器被用於定位系統之中。一般來說，全球導航衛星系統（Global Navigation Satellite System , GNSS）和慣性測量單元（Inertial Measurement Unit, IMU）被組合用於導航系統之中。但是，這種組合導航系統會受到全球導航衛星系統的運行環境和慣性測量單元的誤差特性的限制。如果全球導航衛星系統訊號長時間中斷，比如進入長隧道，或者慣性測量單元沒有很好的校準，都會導致嚴重的定位錯誤。另一種常用的定位方法是使用激光雷達(Light Detection And Ranging, LiDAR)進行定位，其結果可以達到厘米級定位精度。然而，惡劣天氣條件下和環境變化仍然是激光雷達方法需要去克服的重要問題。本文旨在結合全球導航衛星系統與慣性測量單元系統的特點和激光雷達的優勢，借助車輪輪速計。通過擴展卡爾曼濾波器（EKF）融合多傳感器數據並實時實現。與傳統方法相比，多傳感器融合降低了系統對單個傳感器的依賴，提高了定位的應用範圍。這個系統被實現在真實車輛上並在台灣智駕測試實驗室與其周邊一般道路進行測試。最後，論文對實驗結果進行了介紹和分析。

High-precision localization is the most basic and most significant subject of autonomous driving. With the progress of technology, more and more sensors are used in localization system. Generally speaking, the Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) information are used for integrated navigation. However, this integrated navigation method will be limited by the GNSS operating environment and the error characteristics of the IMU. If the GNSS signal is interrupted for a long time, such as entering a long tunnel, or the quality of the IMU is not well calibrated, serious location errors will occur as a result. Another common localization method is to use LiDAR for positioning and the result can reach centimeter positioning accuracy. However, failure during the harsh weather conditions and the changes of environments still is an important issue of LiDAR based method. The thesis aims to integrate the characteristics of GNSS/INS and the advantages of LiDAR, with the assistance of wheel odometry. Though the Extended Kalman Filter(EKF) to fuse multi-sensor data and implement in real time. Compared with the conventional method, fusion of multi-sensor reduces the system's reliance on each single sensor and improves the application range of localization. In the end, the thesis presents and analyses the experimental results.

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