本研究發展一套利用兩個感測器來偵測室內磁場是否受到干擾的方法，並將干擾的程度定義成一個信心指標。透過數值模擬建立此信心指標與磁力計方位角誤差方差之間的關係，再將此關係運用在卡曼濾波器的更新模型，用來修正易受打滑影響的編碼器方位角預測模型，可得較精準的方位角量測訊號。接續利用修正後之方位角量測訊號當作更新資料，用來修正陀螺儀方位角的預測模型，可解決陀螺儀積分漂移的問題，最後得到的方位角估測訊號，同時具有陀螺儀暫態性能佳與磁力計未受干擾穩態性能佳的優點。最後利用延伸型卡曼濾波器定位結合多感測器與攝影機影像資訊來修正移動過程的里程誤差，即可完成精準的定位任務。

This paper develops a method that uses two magnetometers to detect whether the indoor magnetic field is disturbed for mobile robot localization purposes. We define a confidence index to show the degree of interference. The relationship between confidence index and variance of the orientation error derived from magnetometers is established by numerical simulations. Then this relationship is applied to perform the measurement update in Kalman filter to correct the orientation prediction information from wheel encoder. Thus we can get more accurate orientation information from magnetometers under magnetic interference. Then we use this orientation information to correct the error from prediction model which contains the gyroscope as input. Thus we obtain the orientation that has good transient performance due to the use of the gyroscope and good steady-state performance from magnetometers. Finally, we use the extended Kalman filter (EKF) localization to combine multi-sensor data and image processing information from camera to correct the odometry error of mobile robots. Experimental results verify that the proposed method can achieve satisfactory localization performance.

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.3 論文簡介 5
第二章 磁場干擾偵測分析 6
2.1 磁偶極矩簡介 6
2.2 隨機干擾模型分析 8
2.2.1 模型假設 8
2.2.2 模擬結果 11
2.2.3 指標失效機率分析 20
第三章 感測器訊號融合 31
3.1 卡曼濾波器簡介 31
3.2 多維度取樣卡曼濾波器 33
3.3 修正漂移型估測器 36
3.3.1 估測器介紹 36
3.3.2 估測器模擬 39
第四章 影像處理 45
4.1 相機模型與座標轉換介紹 45
4.2 Canny邊緣偵測簡介 52
4.3 Hough直線偵測簡介 54
4.4 特徵點偵測演算法 58
第五章 機器人定位演算法 60
5.1 延伸型卡曼濾波器簡介 60
5.2 延伸型卡曼濾波器定位 62
5.3 室內地磚定位演算法 68
第六章 實驗結果 78
6.1 實驗設備 78
6.1.1 慣性感測器(IMU) 79
6.1.2 微控制器(MCU) 79
6.1.3 攝影機 81
6.2 實驗結果 82
第七章 結論與未來工作 88
7.1 結論 88
7.2 未來工作 89
參考文獻 90

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