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Pedestrian Tracking with Single/Multi IMU Based on Shoe-mounted Inertial Sensors
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박찬국 | - |
dc.contributor.author | 이민수 | - |
dc.date.accessioned | 2017-07-13T06:23:02Z | - |
dc.date.available | 2017-07-13T06:23:02Z | - |
dc.date.issued | 2016-02 | - |
dc.identifier.other | 000000132108 | - |
dc.identifier.uri | https://hdl.handle.net/10371/118504 | - |
dc.description | 학위논문 (박사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 2. 박찬국. | - |
dc.description.abstract | The main objective of this dissertation is to improve the performance of single/multi IMU based PDR (Pedestrian Dead Reckoning) system based on shoe-mounted IMU.
Three algorithms are proposed in this dissertation such as [pedestrian dead reckoning using shoe-mounted IMU], and [kinematic model based PDR for heading correction and lower body motion tracking]. First, algorithms for PDR using foot mounted IMU are proposed. The proposed algorithm is consisted with three algorithms. An advanced stance-phased detection and a step length estimation based on a linearly calibrated Zero velocity Update (ZUPT) and map assisted PDR are proposed. The proposed algorithm works with various movements such as walking, crawling, sideways stepping, and climbing up and down stairs. A single inertial measurement unit imbedded at the subject`s right foot is used. Modified signals to detect stance phase in various motions are proposed and results are provided. Also, linearly calibrated ZUPT algorithm is proposed for increasing step length estimation accuracy in various movement. The algorithm reduces effect of accelerometer bias due to sensor and unstable movement in stance phase. Link-node based map information helps correcting pedestrian`s heading. An Extended Kalman Filter (EKF) is used for fusing the information and estimating pedestrian position and sensor errors. Furthermore, to help complicacy in hall map structures, we propose a Virtual Link (VL) algorithm combined with Virtual Track (VT) algorithm. Experimental results show that the proposed algorithm increase the accuracy of the step length estimation in various movement. Finally, we present a method for finding enhanced heading and position of pedestrian by fusing the ZUPT based PDR and kinematic constraint of lower human body. For integrating these information, the kinematic model of lower human body, which are calculated by using orientation sensors mounted on both thighs and calves, is adopted. Notice that the position of left and right foot cannot be apart because of kinematic constraints of body, the kinematic model generates new measurements for the waist position. The EKF (Extended Kalman Filter) on the waist which estimates and corrects error states uses these measurements and magnetic heading measurements, which enhances heading accuracy. The updated position information is fed into the foot mounted sensors, and reupdate processes are performed to correct the position error of each foot. The proposed update-reupdate technique ensures improved observability of error states and position accuracy consequently. Moreover, the proposed method provides all the information of lower human body, so that it can be applied to motion tracking area more effectively. The effectiveness of the proposed algorithm is verified via experimental results, which shows 1.25% of RPE (Return Position Error) with respect to walking distance is achieved. | - |
dc.description.tableofcontents | Chapter 1.Introduction 1
1.1 Motivation and Background 1 1.2 Objectives and Contributions 6 1.3 Organization 8 Chapter 2. Personal Dead Reckoning 9 2.1 Overview of Pedestrian Dead Reckoning 9 2.2 Step Heading System Based Pedestrian Dead Reckoning 12 2.2.1 Step Detection Algorithm 15 2.2.2 Step Length Estimation Algorithm 19 2.2.3 Heading Estimation 21 2.3 Inertial Navigation System Based Pedestrian Dead Reckoing 22 2.3.1 SDINS Mechanization 24 2.3.2 Extended Kalman Filter 30 2.3.3 INS-EKF-ZUPT 34 2.4 Motion Tracking 41 Chapter 3. Pedestrian Dead Reckoning Using a Single IMU 45 3.1 Introduction 45 3.2 System of PDR Using Shoe-mounted IMU 51 3.3 Stance Phase Detection Algorithm in Various Movement 54 3.4 Linear Calibration Algorithm for the Step Length Estimation 59 3.5 Virtaul Link/Track Algorithm for Hall Condition 64 3.5.1 Virtual Track Algorithm 65 3.5.2 Virtual Link Algorithm 67 3.6 EKF Based Map Assited PDR 69 3.6.1 System Model 71 3.6.2 Measurement Model 73 3.7 Experimental Results 77 3.8 Summary 94 Chapter 4. Kinematic Model Based PDR Using Multi IMU Sensors 95 4.1 Introduction 95 4.2 System Overview 98 4.3 EKF-based PDR and Kinematic Model Fusion 103 4.3.1 Calibration 103 4.3.2 Foot Positioning Using ZUPT 107 4.3.3 Kinematic Model and PDR Fusion for Waist Localization 111 4.3.4 Segment Position Re-update 118 4.3.5 Summarize of EKF for Kinematic Model Based PDR 120 4.3.6 Observability Analysis 125 4.4 Experimental Results 130 4.5 Summary 145 Chapter 5. Conclusions 147 Bibliography 150 국문초록 166 | - |
dc.format | application/pdf | - |
dc.format.extent | 4938608 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Personal Naviagtion System | - |
dc.subject | Pedestrian Dead Reckoning | - |
dc.subject | Motion Tracking | - |
dc.subject | Wearable Sensors | - |
dc.subject.ddc | 621 | - |
dc.title | Pedestrian Tracking with Single/Multi IMU Based on Shoe-mounted Inertial Sensors | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Min Su Lee | - |
dc.description.degree | Doctor | - |
dc.citation.pages | ix, 166 | - |
dc.contributor.affiliation | 공과대학 기계항공공학부 | - |
dc.date.awarded | 2016-02 | - |
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