多感測器與影像資訊融合應用於輪型機器人之室內定位

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。

關鍵字

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

並列摘要

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.

並列關鍵字

sensor fusion ； attitude estimation ； Kalman filter ； feature point detection ； EKF localization

參考文獻

[1] [Online]. Available: http://planning.cs.uiuc.edu/node659.html

[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.

國際替代計量

多感測器與影像資訊融合應用於輪型機器人之室內定位

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