車輛定位是自動駕駛系統中不可缺少的重要資訊，唯有獲得精準的車輛位置才能讓自駕車穩定地朝著目的地前進。現今的車輛定位技術主要藉由光達或相機的感測資訊獲得車輛位置，然而，當周圍環境特徵點不足或是場景發生劇烈變化時，這些定位方法就容易發生誤差。因此，本文提出一個多感測器融合的定位方法，在不易受周圍環境與天氣影響下，提供自動駕駛系統可靠的車輛位置、速度與姿態信息。
全球衛星導航系統 (GNSS) 能在戶外環境中提供公尺級別的定位精度。此種感測方式的更新頻率較慢，且訊號被遮蔽時定位誤差容易放大，因此本文加入慣性量測單元 (IMU) 和魚眼相機進行整合。IMU提供高頻率的車輛動態信息，彌補GNSS資料更新期間資訊量不足的問題。透過魚眼相機360°的環景影像資訊，有助於移除天空中被遮蔽的衛星訊號，降低非視距 (NLOS) 衛星對定位系統的影響。最後，搭配擴增式卡爾曼濾波器 (EKF)，結合各個感測器的感知結果，預估車輛當前的狀態。
本文使用多感測融合的定位技術適用於各種不同的實驗場景，預估的車輛姿態訊息有助於提升後端自動駕駛演算法的表現。本文所提出的定位技術已經成功實現於商用自動駕駛高爾夫球車，並且在揚昇高爾夫鄉村俱樂部進行實車測試。

A well-integrated autonomous driving system (ADS) needs a reliable vehicle position. The autonomous vehicle (AV) can move toward its destination steadily under the premise of an accurate vehicle position. Existing vehicle positioning methods rely on sensor information such as lidars or cameras. However, when there are not many distinguishable features around or the scenarios change dramatically, these vehicle localization methods may be subject to positioning errors. To free from interferences of scenario changing and weather changing, the thesis proposes a multi-sensor fusion method for vehicle positioning, which provides a reliable vehicle position, velocity, and attitude for the ADS.
The Global Navigation Satellite System (GNSS) can provide a meter-level solution in open-sky environments. Different from others, GNSS has a lower refresh rate and its accuracy decreased when the signal is blocked. Therefore, the inertial measurement unit (IMU) and fisheye camera are integrated. IMU can provide high-rate vehicle dynamic information to make up vacancies between GNSS updates. The fisheye camera can provide a 360° surrounding sky image. The impact from non-line-of-sight (NLOS) satellite signals is removed by excluding blocked satellites in the transparent sky. At last, all sensor information is fused in an extended Kalman filter (EKF) for more precise vehicle status.
This thesis proposes a multi-sensor fusion localization method that is applicable in various experiment scenarios. The estimated vehicle status could raise other localization algorithms to another level. The system is implemented in commercial golf carts and tested in Sunrise Golf & Country Club.

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