壓力計輔助之慣性導航感測融合系統製作與驗證

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姓名

黃儒伶(Ju-Ling Huang) 查詢紙本館藏

畢業系所

光機電工程研究所

論文名稱

壓力計輔助之慣性導航感測融合系統製作與驗證
(Development and Validation of an Atmospheric Pressure Sensor-Assisted Inertial Navigation Sensor Fusion System)

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至系統瀏覽論文 (2028-1-31以後開放)

摘要(中)

全球導航衛星系統(GNSS)在現代定位與導航領域中具有廣泛應用，然而，在遮蔽環境中，其訊號可能受阻或失準，導致定位誤差增加。為提升導航系統在受限環境或無GNSS下的定位精度，特別是在垂直方向的高度測量與運動軌跡推算，本研究旨在整合微機電系統慣性感測器(IMU)與壓力計(氣壓計)，建立一套融合導航系統，並透過GNSS數據進行比較與性能驗證。
研究核心方法為融合IMU（加速度計、陀螺儀、磁力計）與氣壓計所取得的感測資料，利用Madgwick演算法進行姿態估計，藉由IMU提供的加速度與角速度資訊進行航跡推算，並結合磁力計以修正航向角，提升短時間內的相對位置估算精度。同時，透過氣壓計所測得的壓力變化進行高度補償，並以誤差狀態卡爾曼濾波器（ESKF）進行高度資訊融合，實現穩定的導航定位功能。由於傳統IMU的漂移誤差與積分誤差會隨時間累積，使得長時間導航的準確性下降，因此特別關注如何藉由氣壓計來減少高度誤差，提升短時間內的相對高度測量精度。此外，針對 GNSS高度數據的穩定性問題，本研究分析了不同測試環境下GNSS與氣壓計的數據表現。設計了多種測試場景，包含靜態測試與車輛運動測試，以評估系統在不同運動條件下的性能表現，對於車輛運動測試，引入 OpenCV影像處理技術，透過錄影擷取儀表板畫面，辨識車速資訊，進一步提升車輛速度估計的準確性，減少純IMU數據可能導致的速度累積誤差。
實驗結果顯示，在高度測量方面，GNSS高度數據的均方根誤差(RMSE)最大可達22m，而氣壓計高度數據的RMSE則僅為3m，其誤差遠小於GNSS，展現了氣壓計作為高度補償工具的優勢。此外，針對整體運動軌跡的誤差進行分析，計算四個Case位置誤差的 PN-RMSE與DPE，分別為1.93%、3.48%、2.53%、2.69%與0.005%、0.42%、1.09%、0.16%，顯示系統的PN-RMSE整體小於3.48%，而DPE低於1.09%，驗證了本研究方法的可行性與有效性。
研究結果證明，本系統已具備初步定位能力，透過 IMU及氣壓計數據的融合，可有效提升定位系統的高度測量精度；OpenCV影像處理辨識車速資訊，可提升車輛速度估計的準確性並提升整體定位精度，在無GNSS或受限時的環境能有效使用此系統定位。此系統可應用於無人機(UAV)、自駕載具、無GNSS環境下的定位等領域，未來可進一步實現實時定位，並透過環境溫度修正、機器學習演算法以及更高精度的IMU感測器等來提升整體導航系統精度，為精確導航提供更可靠的解決方案。

摘要(英)

The Global Navigation Satellite System (GNSS) is widely used in modern positioning and navigation applications. However, in obstructed environments, GNSS signals may be blocked or degraded, resulting in increased positioning errors. To enhance the accuracy of navigation systems under constrained or GNSS-denied environments—particularly in vertical height measurement and motion trajectory estimation—this study aims to integrate Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Units (IMUs) with a barometer to develop a fused navigation system, with its performance evaluated and compared against GNSS data.
The core methodology involves fusing sensor data from the IMU (accelerometer, gyroscope, and magnetometer) and the barometer. The Madgwick algorithm is used for attitude estimation, utilizing accelerometer and gyroscope data for trajectory estimation, while the magnetometer corrects heading drift to improve short-term relative positioning accuracy. In addition, pressure changes measured by the barometer are used for altitude compensation, and an Error-State Kalman Filter (ESKF) is applied to fuse the altitude information, enabling a stable navigation solution.
Due to the drift and integration errors of traditional IMUs, which accumulate over time and degrade long-term accuracy, this study focuses on using barometric data to reduce height estimation errors and improve short-term vertical accuracy. To address the instability of GNSS altitude data, we analyze the performance of GNSS and barometric data under various test environments. Multiple test scenarios were designed, including static tests and vehicle motion tests, to evaluate system performance under different dynamic conditions. For the vehicle motion test, OpenCV image processing techniques were introduced to extract dashboard footage from video recordings and recognize vehicle speed information, further improving speed estimation accuracy and mitigating the velocity drift typically associated with pure IMU data.
Experimental results show that in height estimation, GNSS altitude data yielded a root mean square error (RMSE) of up to 22 meters, while barometric altitude data achieved an RMSE of only 3 meters, highlighting the superiority of barometers in height compensation. Furthermore, trajectory error analysis for four test cases yielded PN-RMSE values of 1.93%, 3.48%, 2.53% and 2.69%, and DPE values of 0.005%, 0.42%, 1.09% and 0.16%, respectively. These results confirm that PN-RMSE remains below 3.48% and DPE under 1.09%, demonstrating the feasibility and effectiveness of the proposed method.
In summary, the developed system exhibits preliminary positioning capabilities. The fusion of IMU and barometer data significantly enhances vertical measurement accuracy, while the use of OpenCV for vehicle speed recognition improves speed estimation and overall positioning performance. This system can be effectively applied in GNSS-denied or constrained environments, such as in unmanned aerial vehicles (UAVs), autonomous vehicles, and indoor or underground navigation. Future work may include real-time implementation, environmental temperature compensation, machine learning algorithms, and higher-precision IMU sensors to further enhance the overall accuracy of the navigation system, providing a more reliable solution for precise navigation.

關鍵字(中)

★ 慣性導航系統
★ 慣性量測單元 IMU
★ 壓力計 (氣壓計 )
★ Madgwick演算法
★ 誤差狀態卡爾曼濾波 (ESKF)

關鍵字(英)

★ Inertial Navigation System
★ Inertial Measurement Unit (IMU)
★ Pressure Sensor(Barometer)
★ Madgwick Algorithm
★ Error-State Kalman Filter

論文目次

摘要 i
Abstract iii
目錄 v
圖目錄 vii
表目錄 x
第一章緒論 1
1-1 研究目的與背景 1
1-2 文獻回顧 2
1-3 論文架構 4
第二章慣性導航系統概論 5
2-1 慣性導航系統 5
2-2 座標系統 6
2-2-1 座標系定義 6
2-2-2 座標系轉換 8
2-3 姿態表示 10
2-3-1 歐拉角 10
2-3-2 旋轉矩陣 11
2-3-3 四元數 13
2-4 導航算法 15
第三章量測系統之感測器原理及整合 18
3-1 感測器原理 18
3-1-1 慣性量測單元(IMU) 19
3-1-2 壓力計 26
3-2 全球導航衛星系統 28
3-3 通訊協定及傳輸架構 31
3-4 無線實驗裝置整合 35
3-4-1 量測模組 35
3-4-2 藍芽傳輸模組 39
3-5 無線慣性導航感測裝置及系統架構 40
3-6 人機操作介面 43
3-6-1 訊號接收操作介面 43
3-6-2 慣性感測器校正 45
第四章系統性能及車輛定位實驗與結果討論 50
4-1 性能驗證及實驗架設 50
4-1-1 模擬訊號驗證 50
4-1-2 壓力計性能驗證 53
4-1-3 Python Open CV指針儀表盤視覺讀取 54
4-2 道路實驗結果驗證及GPS比較分析 57
Case1直線路徑 57
Case2矩形路徑(中大校園矩形路徑) 59
Case3環形路徑(校門外之環形坡道) 61
Case4不規則路徑 63
第五章結論與未來展望 67
5-1 結論 67
5-2 未來展望 67
參考文獻 68

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指導教授

潘敏俊

審核日期

2025-3-27

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