AntIMU: 可互相合作之即時動作偵測的微小慣性測量模組

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研究生：	王維寬 Wang,Wei Kuan
論文名稱：	AntIMU: 可互相合作之即時動作偵測的微小慣性測量模組 AntIMU: a Real-Time Motion-Tracking System Based on Miniature Collaborative IMU Modules
指導教授：	周百祥 Pai H. Chou
口試委員:	周志遠 Jerry Chou 蔡明哲 Ming-Jer Tsai
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	40
中文關鍵詞：	慣性測量單元、軌跡追蹤、加速規、陀螺儀
外文關鍵詞：	IMU, trajectory tracking, accelerometer, gyroscope
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AntIMU是一套雙六軸感測器合作式慣性測量系統，其包含微小型、即時性、無線式等特性. 它的資料來源是兩組六軸慣性感測器，輸出的是一個在三維空間固體移動的方向與軌跡。綜合過的結果降低了漂浮錯誤與雙次積分導致累積的誤差，改善了輸出結果的準確度。我們實驗了兩種將慣性感測器佈設的模式: 「棒形IMU」把兩組同方向的感測器分別裝在一根棒子的兩端，而「梯形IMU」把兩組不同方向的感測器裝在一個梯形的的兩邊。AntIMU實作使用的平台是EcoMini，是一套含無線通訊功能微處理器與九軸慣性感測器的微小型嵌入式系統平台。實驗結果顯示，棒形與梯形IMU比單組EcoMini追綜數個動作的準確度都更高。並且我們發現，主要導致IMU結果誤差的因素並非感測器雜訊，而是物體移動中造成的重力加速度估計錯誤，進而造成傾斜角度估計錯誤。

AntIMU is a way of building a miniature real-time wireless inertial measurement unit by using a pair of 6-DoF inertial sensors collaboratively to achieve better accuracy. AntIMU measures the orientation and trajectory of an object in 3D space by combining two sets of 6-DoF inertial sensors with gyroscope and accelerometer. The combined result improves the tracking accuracy by reducing errors due to drifts and error accumulation from double integration. We experimented with two topologies called Stick-IMU and Frustum-IMU, where we mount the two sets of inertial sensors in the same orientation on two ends of a pole and in different orientations on a trapezoid frustum, respectively. We utilize the orientation diversity and the spatial diversity of relative placement of sensors to get the more precise IMU trajectory estimation.
We implemented AntIMU using multiple units of EcoMini, a miniature wireless sensor platform with a radio-enabled microcontroller and a 6-DoF inertial sensor. Experimental results show these two AntIMU to be higher accuracy than the single-EcoMini IMU over several motion patterns. Furthermore, we found that the main cause of errors in IMU trajectory estimation is not sensor noise but the gravity cancellation caused by object movement.

Contents i
Acknowledgments vi
Introduction 1
1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Orientation Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Spatial Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Others Diversities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Related Work 5
Background Theory 7
1 Euler Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Quaternion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Filters for IMU Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1 Mahony Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
System Architecture and Implementation 13
1 Node Subsystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Host Subsystem of Single-IMU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Calculation of Linear Displacement . . . . . . . . . . . . . . . . . . . . . . 15
i
2.3 Zero Velocity Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Dual-EcoMini Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1 Frustum-IMU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Stick-IMU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Evaluation 30
1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2 Orientation Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3 Trajectory Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Conclusions and Future Work 37
1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
                                

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