9 research outputs found
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Robust Indoor Pedestrian Backtracking Using Magnetic Signatures and Inertial Data
Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.
We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system
μ¬μ©μ μν©μΈμ§ λ₯λ¬λμ μ¬μ©ν GPS λ°μ‘ν / κ΄μ± μΌμ κ²°ν© μ€λ§νΈν° 보νμ νλ²
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : 곡과λν κΈ°κ³ν곡곡νλΆ, 2019. 2. μ¬μ¬μ΅.λ³Έ λ
Όλ¬Έμμλ μ€λ§νΈν° Galaxy S8 νκ²½μμ GPS / INS κ²°ν© λ³΄νμ νλ²μ μννμμΌλ©°, μ€λ§νΈν° μΌμμ νΉμ±μ μμΈν λΆμνμλ€. μ΄μ μ΅κ·Ό 곡κ°λ Android GNSS API λ₯Ό μ¬μ©νμ¬ GPS μμλ°μ΄ν°λ₯Ό νλ²μ μ΄μ©νλ©΄μ, Cycle slip μ 보μ ν Carrier phase λ₯Ό μ΄μ©ν μλ κ²°μ λ²μ μ¬μ©νμλ€. μ΄λ‘ μΈν΄ κΈ°μ‘΄μ NMEA GPS λ₯Ό μ¬μ©ν λ°©μμ μ€λ§νΈν° 보νμ νλ²λ³΄λ€ μ λ°ν μμΉ, μλ νλ²μ΄ κ°λ₯νμκ³ , μ±λ₯μ ν₯μ μμΌ°λ€. λν μ¬μ©μ μν© λΆμμ΄ κ°λ₯ν λΆλ₯ λ₯λ¬λ κΈ°λ²μ μ¬μ©νμ¬ κ° λ³΄ν μν©μ λ°λ₯Έ λΆλ₯κ°μ§κ° κ°λ₯νμμ 보μμΌλ©°, LSTM μ μ
λ ₯λΆλΆμ λ³νν λͺκ°μ§ λ₯λ¬λ λͺ¨λΈμ μ±λ₯μ λΉκ΅νμλ€. μ΄λ₯Ό ν΅ν΄μ μ¬μ©μμ 보ν μν©μ λ°λ₯Έ μ μμ 보νμ νλ² νλΌλ©ν° κ²°μ μ΄ κ°λ₯ν¨μ κ°λ₯μ±μ 보μλ€.In this research, the overall construction of the smartphone GPS / INS pedestrian dead reckoning system is detaily described with considering the smartphone sensor measurement properties. Also, the recent android GNSS API which can provide the raw GPS measurement is used. With carrier phase, the cycleslip compensated velocity determination is considered. As a result, the carrier phase /INS integrated pedestrian dead reckoning shows the more precise navigation accuracy than NMEA. Moreover, The deep learning approach is applied in the user context classification to change the parameters in the pedestrian dead reckoning system. The author compares the effect of several transformed inputs for the LSTM model and validate each classification performances.Abstract i
Contents ii
List of Figures iv
List of Tables vii
Chapter 1. Introduction 1
1.1 Motivation and Backgrounds 1
1.2 Research Purpose and Contribution 3
1.3 Contents and Methods of Research 3
Chapter 2. Smartphone GPS / INS measurements analysis 4
2.1 Smartphone GNSS measurements 4
2.1.1 Android Raw GNSS Measurements API 4
2.1.2 Raw GPS Measurements Properties 7
2.1.3 Smartphone NMEA Location Provider 8
2.1.4 Pseudorange Based Position Estimation 10
2.1.5 Position Determination Experiment 11
2.2 Smartphone INS Measurements 12
2.2.1 Android Sensor Manager API 12
2.2.2 INS Measurements Properties 13
2.2.3 Noise level, Constant bias, Scale factor, Calibration 14
2.2.4. Accelerometer, Gyroscope Calibration Experiment 17
2.2.5 Magnetometer Ellipse Fitting Calibration 22
2.2.6 Random Bias, Allan Variance Exiperiment 25
2.3 Developed Android Smartphone App 30
Chapter 3. Pedestrian Dead Reckoning 31
3.1 Pedestrian Dead-Reckoning System 31
3.1.1 Attitude Determination Quaternion Kalman Filter 32
3.1.2 Attitude Determination Simulation , Experiment 35
3.1.3 Walking Detection 39
3.1.4 Step Counting, Stride Length 41
3.1.5 Pedestrian Dead Reckoning Experiment 45
Chapter 4. Carrier phase / INS integrated Pedestrian Dead Reckoning 50
4.1 Carrier phase Cycleslip Compensation & Velocity Determination 50
4.1.1 Carrier phase Cycleslip Compensation 50
4.1.2 Android Environment Cycle slip Detection 51
4.1.3 False Alarm & Miss Detection Analysis 55
4.1.4 Doppler, Carrier Based Velocity Estimation 56
4.1.5 Cycle slip Compensation & Velocity Determination Experiment 58
4.2 Raw GPS / INS Integrated Pedestrian Dead Reckoning 63
4.2.1 GPS / INS Integration 63
4.2.2 Position Determination Extended Kalman Filter 65
4.2.3 Raw GPS / INS Integrated Pedestrian Dead Reckoning Experiment 66
Chapter 5. User Context Classification Deep learning for Adaptive PDR 69
5.1 Smartphone Location / Walking Context Classification 69
5.1.1 Smartphone Location / Walking Context Dataset 69
5.1.2 Deep Learning Models 71
5.1.3 Comparison of Input Transformations 72
Chapter 6. Conclusion & Future work 76
Chapter 7. Bibliography 77Maste
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All the Way There and Back: Inertial-Based, Phone-in-Pocket Indoor Wayfinding and Backtracking Apps for Blind Travelers
Robust localization with wearable sensors
Measuring physical movements of humans and understanding human behaviour is useful in a variety of areas and disciplines. Human inertial tracking is a method that can be leveraged for monitoring complex actions that emerge from interactions between human actors and their environment. An accurate estimation of motion trajectories can support new approaches to pedestrian navigation, emergency rescue, athlete management, and medicine. However, tracking with wearable inertial sensors has several problems that need to be overcome, such as the low accuracy of consumer-grade inertial measurement units (IMUs), the error accumulation problem in long-term tracking, and the artefacts generated by movements that are less common. This thesis focusses on measuring human movements with wearable head-mounted sensors to accurately estimate the physical location of a person over time. The research consisted of (i) providing an overview of the current state of research for inertial tracking with wearable sensors, (ii) investigating the performance of new tracking algorithms that combine sensor fusion and data-driven machine learning, (iii) eliminating the effect of random head motion during tracking, (iv) creating robust long-term tracking systems with a Bayesian neural network and sequential Monte Carlo method, and (v) verifying that the system can be applied with changing modes of behaviour, defined as natural transitions from walking to running and vice versa. This research introduces a new system for inertial tracking with head-mounted sensors (which can be placed in, e.g. helmets, caps, or glasses). This technology can be used for long-term positional tracking to explore complex behaviours