近年來，自動駕駛的技術越來越受重視，自動駕駛車輛不僅能夠降低人力成本，也能降低人為疏失所造成的事故發生，為了達到自動駕駛的功能，精確的車輛定位導航為一相當不可或缺的技術。對於一般的車輛導航而言，GNSS接收器、慣性感測元件與車上的輪速計，常被用來實現感測器融合定位技術。這些感測器具有互補的性質，搭配適當的融合演算法，藉此提供更佳的準確性、完整性與可用性。然而，只使用上述的感測器，難以達到自動駕駛的所需求的定位標準，為了更進一步提升定位的精準度與強健性，通常會再結合相機、雷射雷達並搭配點雲甚至是精密圖資。利用相機或雷射雷達掃描的結果與點雲或精密圖資匹配，估算當前車輛的姿態與位置，被稱為基於地圖資訊的定位。
本論文以LiDAR搭配點雲地圖作為車輛定位的基礎，並結合GNSS、IMU和輪速計增加系統的強健性。為了要即時多工處理各項感測器的信息，本研究以機器人作業系統作為核心，負責演算法運算與感測器資料之整合與應用，並利用擴展式卡爾曼濾波器進行車輛狀態的量測估計與更新，實現較高精度且強健的即時定位系統。但不可忽視的是每種感測器都可能有失靈情況發生，為了驗證此系統的強健性，本論文亦探討在特定感測器失效或異常的情況下，系統的定位準確度，並且在不同的環境下實測。

In recent years, the technology of autonomous vehicle is attracting more and more attention. In order to achieve the function of autonomous driving, precise vehicle positioning is an essential technology. For the navigation of a vehicle, Global Satellite Navigation System (GNSS) and Inertial Measurement Unit (IMU), and wheel speed sensor are often employed to implement a fused navigation solution. These sensors have complementary properties, coupled with appropriate fusion algorithms, to provide better accuracy, integrity, and availability. However, using only the sensors mentioned above, it is difficult to achieve the required positioning performance for autonomous driving.
In order to further improve the accuracy and robustness of the positioning system. The thesis studies the use of LiDAR-based navigation system for autonomous vehicle with 3D point cloud map. To match scans and point cloud map, normal distribution transformation algorithm is adopted for scans aligning. Moreover, to increase the robustness of the system, GNSS, IMU and odometer are also employed in this system. In practice, it might happen that sensors cannot work or stuck in abnormal status. To verify the robustness of this system, this thesis also evaluates the accuracy of the positioning of the system in the case of failure or abnormality of specific sensor.
Finally, we performed several experiments in different scenarios. The results show that the navigation system with map aided, the system can keep a high performance, even if the vehicle drives in GNSS-denied environment.

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