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研究生:侯秉君
研究生(外文):HOU,BING-JYUN
論文名稱:以慣性與光流感測器實現 行人定位
論文名稱(外文):Pedestrian Dead Reckoning Realized by Inertial and Optical Flow Sensors
指導教授:鄭湘原
指導教授(外文):JENG, SYANG-YWAN ERIK
口試委員:陳原逢周武清
口試委員(外文):CHEN, YUAN-FENGCHCU, WU-CHING
口試日期:2022-07-30
學位類別:碩士
校院名稱:中原大學
系所名稱:電子工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:56
中文關鍵詞:光流計慣性感測器類神經網路行人航向推算
外文關鍵詞:optical flow meterinertial sensorartificial neural networkpedestrian dead reckoning
DOI:10.6840/cycu202201540
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目錄
中文摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章緒論 1
1-1研究背景 1
1-2研究動機與目的 2
1-3 文獻回顧 2
1-3-1 OFS相關應用 2
1-3-2 IMU航向角度 5
1-3-3 誤差修正神經網路架構 6
1-4 研究方法 8
1-5 論文架構 9
第二章 航向推算架構 10
2-1 光流法路徑追蹤 10
2-1-1 光流法位移量測原理 10
2-1-2 OFS系統介紹 11
2-2 慣性感測方位角推算 13
2-2-1三維慣性量測 13
2-2-2 IMU推算姿態角度 17
2-3 濾波器種類與原理 18
2-3-1移動平均濾波器(moving average filter) 18
2-3-2卡爾曼濾波器(Kalman filter) 19
2-4路徑誤差神經網路架構 20
第三章 路徑航向推算系統 21
3-1 系統架構 21
3-2 系統整合驗證 25
3-2-1 系統設計 25
3-2-2 系統安裝與運作 26
3-3 系統感測器可靠度與穩定性 27
3-3-1 OFS位移追蹤測試 27
3-3-2.IMU方位角測量 28
第四章 實際場域路徑測試與誤差分析 30
4-1實際路徑量測 30
4-2位移量分析 33
4-2-1 OFS誤差 33
4-2-2 OFS位移量修正 35
4-3 方位角分析 36
4-3-1 方位角度誤差 36
4-3-2 方位角濾波修正 38
4-4 類神經網路修正 40
第五章結論與展望 43
5-1 結論 43
5-2 未來展望 43
參考文獻 45


圖目錄
圖1- 1 OFS進行無人車路徑位移估算[1-5] 3
圖1- 2 以影像觀測光流向量[1-6] 4
圖1- 3 顯示精準度對表面特徵與距離的關係圖[1-7] 4
圖1- 4 CNN與LSTM結合模型架構 7
圖1- 5結合MLP網路架構預測行人未來動作 8
圖2- 1 以連續影像進行光流向量分析示意 10
圖2- 2 光流對物體或是光源的位移追蹤示意 11
圖2- 3 (a)感測器組件的分解圖(b)感測器組件的實體照片 12
圖2- 4 分辨率差異比較 13
圖2- 5 IMU數據輸出示意圖 14
圖2- 6 加速度計感測原理 16
圖2- 7 陀螺儀感測器原理 17
圖2- 8 以三維角速度進行積分求得角度 17
圖2- 9 行人定位三維坐標系統 18
圖2- 10 移動平均濾波修正角速度 19
圖2- 11以類神經網路誤差修正流程架構 20
圖3- 1 系統架構 21
圖3- 2 MCU基礎內部架構 22
圖3- 3 藍芽無線傳輸系統 23
圖3- 4 SPI數據讀取操作時序 24
圖3- 5 SPI數據寫入操作時序 24
圖3- 6 I2C傳輸協定架構 25
圖3- 7 系統內部配置 26
圖3- 8 定位追蹤量測裝置設置安裝示意 26
圖3- 9 將OFS在不同高度的圖像採集 27
圖3- 10各步長OFS累積位移量隨時間的變化 28
圖3- 11 角度誤差修正比較 29
圖4- 1 短距離場域示意 30
圖4- 2 長距離場域示意 31
圖4- 3短距離路徑量測與實際誤差的比較 32
圖4- 4 長方形路徑量測與實際誤差的比較 33
圖4- 5 80cm步長行走抬腳的OFS位移隨時間的變化 34
圖4- 6 原地抬腿的瞬時位移隨時間的變化 34
圖4- 7 不同行走步長測量瞬時位移隨時間的變化量 35
圖4- 8 以閥值進行修正後的二維路徑 36
圖4- 9 以直線行走測量角度隨時間的變化 37
圖4- 10在身體晃動下測量角度隨時間的變化 38
圖4- 11 角速度隨時間的變動: 39
圖4- 12 經移動平滑濾波修正後角度隨時間的變化 40
圖4- 13 誤差修正網路之架構 41
圖4- 14直線路徑誤差修正前後之結果比較 42
圖4- 15 方形路徑誤差修正前後之結果比較 42


表目錄

表1- 1 GPS、Beacon與本論文裝置之差異 1
表1- 2 三種系統路徑推算誤差比較 6
表2- 1多種IMU規格比較分析[2-7] 15
表3- 1 OFS規格 27


第一章
[1-1] McCarthy, C.; Bames, N. Performance of optical flow techniques for indoor navigation with a mobile robot. In Proceedings of the 2004 IEEE International Conference on Robotics and Automation, New Orleans, LA, USA, 26 April–1 May 2004; pp. 5093–5098.
[1-2] Nagatani, K.; Tachibana, S.; Sofne, M.; Tanaka, Y. Improvement of odometry for omnidirectional vehicle using optical flow information. In Proceedings of the 2000 IEEE/RSJ International Conference on the Intelligent Robots and Systems, Takamatsu, Japan, 30 October–5 November 2000; pp. 468–473.
[1-3] Ng, T. The optical mouse as a two-dimensional displacement sensor. Sens. Actuators A Phys. 2003,107, 21–25.
[1-4] Bonarini, A.; Matteucci, M.; Restelli, M. Automatic error detection and reduction for an odometric sensor based on two optical mice. In Proceedings of the 2005 IEEE International Conference on
[1-5] Yi DH, Lee TJ, Cho DI. Afocal optical flow sensor for reducing vertical height sensitivity in indoor robot localization and navigation. Sensors (Basel). 2015 May 13;15(5):11208-21. doi: 10.3390/s150511208. PMID: 25985164; PMCID: PMC4481980.
[1-6] J. Qian, L. Pei, D. Zou, K. Qian and P. Liu, "Optical flow based step length estimation for indoor pedestrian navigation on a smartphone," 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014, 2014, pp. 205-211, doi: 10.1109/PLANS.2014.6851377.
[1-7] Minoni, U.; Signorini, A. Low-cost optical motion sensors: An experimental characterization.Sens. Actuators A Phys. 2006, 128, 402–408.
[1-8] H. Ju, M. S. Lee, S. Y. Park, J. W. Song and C. G. Park, "A pedestrian dead-reckoning system that considers the heel-strike and toe-off phases when using a foot-mounted IMU", Meas. Sci. Technol., vol. 27, no. 1, Dec. 2015.
[1-9] H. Zhang et al., "Axis-exchanged compensation and gait parameters analysis for high accuracy indoor pedestrian dead reckoning", J. Sensors, vol. 2015, no. 2, Jan. 2015.
[1-10] T.-N. Do, R. Liu, C. Yuen, M. Zhang and U.-X. Tan, "Personal dead reckoning using IMU mounted on upper torso and inverted pendulum model", IEEE Sensors J., vol. 16, no. 21, pp. 7600-7608, Nov. 2016.
[1-11] S. Zihajehzadeh and E. J. Park, "Regression model-based walking speed estimation using wrist-worn inertial sensor", PLoS ONE, vol. 11, no. 10, Oct. 2016.
[1-12] N. Yu, Y. Li, X. Ma, Y. Wu and R. Feng, "Comparison of Pedestrian Tracking Methods Based on Foot- and Waist-Mounted Inertial Sensors and Handheld Smartphones," in IEEE Sensors Journal, vol. 19, no. 18, pp. 8160-8173, 15 Sept.15, 2019, doi: 10.1109/JSEN.2019.2919721.
[1-13] João Paulo Silva do Monte Lima, Hideaki Uchiyama, and Rin-ichiro Taniguchi, “End-to-End Learning Framework for IMU-Based 6-DOF Odometry,” 31/August/2019
[1-14] M. Goldhammer, S. Köhler, K. Doll and B. Sick, "Camera based pedestrian path prediction by means of polynomial least-squares approximation and multilayer perceptron neural networks," 2015 SAI Intelligent Systems Conference (IntelliSys), 2015, pp. 390-399, doi: 10.1109/IntelliSys.2015.7361171.
第二章
[2-1] Jinsong Zhu, Ziyue Lu, Chi Zhang. (2022) A marker-free method for structural dynamic displacement measurement based on optical flow. Structure and Infrastructure Engineering 18:1, pages 84-96.
[2-2] D. Honegger, L. Meier, P. Tanskanen and M. Pollefeys, "An open source and open hardware embedded metric optical flow CMOS camera for indoor and outdoor applications," 2013 IEEE International Conference on Robotics and Automation, 2013, pp. 1736-1741, doi: 10.1109/ICRA.2013.6630805.
[2-3] ANAND~X5 P. 1989. A computational framework and an algorithm for the measurement of visual motion. Int. J. Comput. -Vzszon 2,283-310.
[2-4] Labutov, I., Jaramillo, C. & Xiao, J. Generating near-spherical range panoramas by fusing optical flow and stereo from a single-camera folded catadioptric rig. Machine Vision and Applications 24, 133–144 (2013).
[2-5] K. Daniilidis, A. Makadia and T. Bulow, "Image processing in catadioptric planes: spatiotemporal derivatives and optical flow computation," Proceedings of the IEEE Workshop on Omnidirectional Vision 2002. Held in conjunction with ECCV'02, 2002, pp. 3-10, doi: 10.1109/OMNVIS.2002.1044483
[2-6] Cardarelli, Donato. "An integrated MEMS inertial measurement unit." 2002 IEEE Position Location and Navigation Symposium (IEEE Cat. No. 02CH37284). IEEE, 2002.
[2-7] A. Patarot, M. Boukallel and S. Lamy-Perbal, "A case study on sensors and techniques for pedestrian inertial navigation," 2014 International Symposium on Inertial Sensors and Systems (ISISS), 2014, pp. 1-4, doi: 10.1109/ISISS.2014.6782527.
[2-8]B. Abbott and S. E. Lyshevski, "Signal processing in MEMS inertial measurement units for dynamic motional control," 2016 IEEE 36th International Conference on Electronics and Nanotechnology (ELNANO), 2016, pp. 309-314, doi: 10.1109/ELNANO.2016.7493074.
[2-9]Skog, Isaac, and Peter Händel. "Calibration of a MEMS inertial measurement unit." XVII IMEKO world congress. 2006.
[2-10] G. G. Redhyka, D. Setiawan and D. Soetraprawata, "Embedded sensor fusion and moving-average filter for Inertial Measurement Unit (IMU) on the microcontroller-based stabilized platform," 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), 2015, pp. 72-77, doi: 10.1109/ICACOMIT.2015.7440178.
[2-11] T. Lisini Baldi, F. Farina, A. Garulli, A. Giannitrapani and D. Prattichizzo, "Upper Body Pose Estimation Using Wearable Inertial Sensors and Multiplicative Kalman Filter," in IEEE Sensors Journal, vol. 20, no. 1, pp. 492-500, 1 Jan.1, 2020, doi: 10.1109/JSEN.2019.2940612.
[2-12] F. M. Mirzaei and S. I. Roumeliotis, "A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation," in IEEE Transactions on Robotics, vol. 24, no. 5, pp. 1143-1156, Oct. 2008, doi: 10.1109/TRO.2008.2004486.
第三章
[3-1] Parai, Manas Kumar et al. “An Overview of Microcontroller Unit : From Proper Selection to Specific Application.” (2013).
[3-2] F. Leens, "An introduction to I2C and SPI protocols," in IEEE Instrumentation & Measurement Magazine, vol. 12, no. 1, pp. 8-13, February 2009, doi: 10.1109/MIM.2009.4762946.
[3-3] Campbell, Jason, et al. "A robust visual odometry and precipice detection system using consumer-grade monocular vision." Proceedings of the 2005 IEEE International Conference on robotics and automation. IEEE, 2005.
[3-4] Alvarez, J.C.; Alvarez, D.; López, A.; González, R.C. Pedestrian Navigation Based on a Waist-Worn Inertial Sensor. Sensors 2012, 12, 10536-10549.
[3-5] McGinnis, RS, Cain, SM, Davidson, SP, Vitali, RV, McLean, SG, & Perkins, NC. "Validation of Complementary Filter Based IMU Data Fusion for Tracking Torso Angle and Rifle Orientation." Proceedings of the ASME 2014 International Mechanical Engineering Congress and Exposition. Volume 3: Biomedical and Biotechnology Engineering. Montreal, Quebec, Canada. November 14–20, 2014. V003T03A052. ASME.
[3-6] H. Fourati, N. Manamanni, L. Afilal and Y. Handrich, "Position estimation approach by Complementary Filter-aided IMU for indoor environment," 2013 European Control Conference (ECC), 2013, pp. 4208-4213, doi: 10.23919/ECC.2013.6669211.
第四章
[4-1] Bell, Steven. "High-precision robot odometry using an array of optical mice." Oklahoma Christian University 24 (2011).
[4-2] P. Goyal, V. J. Ribeiro, H. Saran and A. Kumar, "Strap-down Pedestrian Dead-Reckoning system," 2011 International Conference on Indoor Positioning and Indoor Navigation, 2011, pp. 1-7, doi: 10.1109/IPIN.2011.6071935.
[4-3] K. Wen, K. Yu, Y. Li, S. Zhang and W. Zhang, "A New Quaternion Kalman Filter Based Foot-Mounted IMU and UWB Tightly-Coupled Method for Indoor Pedestrian Navigation," in IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4340-4352, April 2020, doi: 10.1109/TVT.2020.2974667

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