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研究生:許勝傑
研究生(外文):Hsu, Sheng-Chieh
論文名稱:多感測器與影像資訊融合應用於輪型機器人之室內定位
論文名稱(外文):Sensor Fusion of Multi-Sensor and Vision Data for Indoor Localization of Mobile Robots
指導教授:葉廷仁
指導教授(外文):Yeh, Ting-Jen
口試委員:林沛群劉承賢
口試委員(外文):Lin, Pei-ChunLiu, Cheng-Hsien
口試日期:2017-07-28
學位類別:碩士
校院名稱:國立清華大學
系所名稱:動力機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:92
中文關鍵詞:感測器融合姿態估測卡曼濾波器特徵點偵測延伸型卡曼濾波器定位
外文關鍵詞:sensor fusionattitude estimationKalman filterfeature point detectionEKF localization
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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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[2] Y. Dobrev, S. Flores and M. Vossiek, "Multi-modal sensor fusion for indoor mobile robot pose estimation," 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), Savannah, GA, 2016, pp. 553-556.

[3] J. Campbell, R. Sukthankar, I. Nourbakhsh and A. Pahwa, "A Robust Visual Odometry and Precipice Detection System Using Consumer-grade Monocular Vision," Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 3421-3427.

[4] A. Novitsky and D. Yukhimets, "The navigation method of wheeled mobile robot based on data fusion obtained from onboard sensors and camera," Control, Automation and Systems (ICCAS), 2015 15th International Conference on, Busan, 2015, pp. 574-579.

[5] J. R. Goulding, "Biologically-inspired image-based sensor fusion approach to compensate gyro sensor drift in mobile robot systems that balance," Multisensor Fusion and Integration for Intelligent Systems (MFI), 2010 IEEE Conference on, Salt Lake City, UT, 2010, pp. 102-108.

[6] H. Y. Chung, C. C. Hou and Y. S. Chen, "Indoor Intelligent Mobile Robot Localization Using Fuzzy Compensation and Kalman Filter to Fuse the Data of Gyroscope and Magnetometer," in IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6436-6447, Oct. 2015.

[7] J. Jung, S. M. Lee and H. Myung, "Indoor Mobile Robot Localization and Mapping Based on Ambient Magnetic Fields and Aiding Radio Sources," in IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 7, pp. 1922-1934, July 2015.

[8] D. Navarro and G. Benet, "Magnetic map building for mobile robot localization purpose," 2009 IEEE Conference on Emerging Technologies & Factory Automation, Mallorca, 2009, pp. 1-4.

[9] B. Gozick, K. P. Subbu, R. Dantu and T. Maeshiro, "Magnetic Maps for Indoor Navigation," in IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 12, pp. 3883-3891, Dec. 2011.

[10] D. Roetenberg, H. J. Luinge, C. T. M. Baten and P. H. Veltink, "Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 3, pp. 395-405, Sept. 2005.

[11] Muhammad Haris Afzal, Valerie Renaudin, and Gerard Lachapelle, "Multi-Magnetometer Based Perturbation Mitigation for Indoor Orientation Estimation," in NAVIGATION, Journal of the Institute of Navigation, Volume 58, Number 4, pp. 279-292, December 2011.

[12] [Online]. Available: https://en.wikipedia.org/wiki/Magnetic_dipole

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[18] [Online]. Available: https://www.arduino.cc/en/Main/arduinoBoardMega2560

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