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

적분 및 매개변수 기법 융합을 이용한 스마트폰 다중 동작에서 보행 항법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계항공공학부, 2020. 8. 박찬국.In this dissertation, an IA-PA fusion-based PDR (Pedestrian Dead Reckoning) using low-cost inertial sensors is proposed to improve the indoor position estimation. Specifically, an IA (Integration Approach)-based PDR algorithm combined with measurements from PA (Parametric Approach) is constructed so that the algorithm is operated even in various poses that occur when a pedestrian moves with a smartphone indoors. In addition, I propose an algorithm that estimates the device attitude robustly in a disturbing situation by an ellipsoidal method. In addition, by using the machine learning-based pose recognition, it is possible to improve the position estimation performance by varying the measurement update according to the poses. First, I propose an adaptive attitude estimation based on ellipsoid technique to accurately estimate the direction of movement of a smartphone device. The AHRS (Attitude and Heading Reference System) uses an accelerometer and a magnetometer as measurements to calculate the attitude based on the gyro and to compensate for drift caused by gyro sensor errors. In general, the attitude estimation performance is poor in acceleration and geomagnetic disturbance situations, but in order to effectively improve the estimation performance, this dissertation proposes an ellipsoid-based adaptive attitude estimation technique. When a measurement disturbance comes in, it is possible to update the measurement more accurately than the adaptive estimation technique without considering the direction by adjusting the measurement covariance with the ellipsoid method considering the direction of the disturbance. In particular, when the disturbance only comes in one axis, the proposed algorithm can use the measurement partly by updating the other two axes considering the direction. The proposed algorithm shows its effectiveness in attitude estimation under disturbances through the rate table and motion capture equipment. Next, I propose a PDR algorithm that integrates IA and PA that can be operated in various poses. When moving indoors using a smartphone, there are many degrees of freedom, so various poses such as making a phone call, texting, and putting a pants pocket are possible. In the existing smartphone-based positioning algorithms, the position is estimated based on the PA, which can be used only when the pedestrian's walking direction and the device's direction coincide, and if it does not, the position error due to the mismatch in angle is large. In order to solve this problem, this dissertation proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the pose recognized through machine learning technique, the position is updated in consideration of the direction calculated using PCA (Principal Component Analysis) and the step length obtained through the PA. It can be operated robustly even in various poses that occur. Through experiments considering various operating conditions and paths, it is confirmed that the proposed method stably estimates the position and improves performance even in various indoor environments.본 논문에서는 저가형 관성센서를 이용한 보행항법시스템 (PDR: Pedestrian Dead Reckoning)의 성능 향상 알고리즘을 제안한다. 구체적으로 보행자가 실내에서 스마트폰을 들고 이동할 때 발생하는 다양한 동작 상황에서도 운용될 수 있도록, 매개변수 기반 측정치를 사용하는 적분 기반의 보행자 항법 알고리즘을 구성한다. 또한 타원체 기반 자세 추정 알고리즘을 구성하여 외란 상황에서도 강인하게 자세를 추정하는 알고리즘을 제안한다. 추가적으로 기계학습 기반의 동작 인식 정보를 이용, 동작에 따른 측정치 업데이트를 달리함으로써 위치 추정 성능을 향상시킨다. 먼저 스마트폰 기기의 이동 방향을 정확하게 추정하기 위해 타원체 기법 기반 적응 자세 추정을 제안한다. 자세 추정 기법 (AHRS: Attitude and Heading Reference System)은 자이로를 기반으로 자세를 계산하고 자이로 센서오차에 의해 발생하는 드리프트를 보정하기 위해 측정치로 가속도계와 지자계를 사용한다. 일반적으로 가속 및 지자계 외란 상황에서는 자세 추정 성능이 떨어지는데, 추정 성능을 효과적으로 향상시키기 위해 본 논문에서는 타원체 기반 적응 자세 추정 기법을 제안한다. 측정치 외란이 들어오는 경우, 외란의 방향을 고려하여 타원체 기법으로 측정치 공분산을 조정해줌으로써 방향을 고려하지 않은 적응 추정 기법보다 정확하게 측정치 업데이트를 할 수 있다. 특히 외란이 한 축으로만 들어오는 경우, 제안한 알고리즘은 방향을 고려해 나머지 두 축에 대해서는 업데이트 해줌으로써 측정치를 부분적으로 사용할 수 있다. 레이트 테이블, 모션 캡쳐 장비를 통해 제안한 알고리즘의 자세 성능이 향상됨을 확인하였다. 다음으로 다양한 동작에서도 운용 가능한 적분 및 매개변수 기법을 융합하는 보행항법 알고리즘을 제안한다. 스마트폰을 이용해 실내를 이동할 때에는 자유도가 크기 때문에 전화 걸기, 문자, 바지 주머니 넣기 등 다양한 동작이 발생 가능하다. 기존의 스마트폰 기반 보행 항법에서는 매개변수 기법을 기반으로 위치를 추정하는데, 이는 보행자의 진행 방향과 기기의 방향이 일치하는 경우에만 사용 가능하며 일치하지 않는 경우 자세 오차로 인한 위치 오차가 크게 발생한다. 이러한 문제를 해결하기 위해 본 논문에서는 적분 기반 기법을 기반으로 상태변수를 구성하고 매개변수 기법을 통해 나오는 위치 벡터를 측정치로 사용하는 알고리즘을 제안한다. 만약 기계학습을 통해 인식한 동작을 바탕으로 진행 방향과 기기 방향이 일치하지 않는 경우, 주성분 분석을 통해 계산한 진행방향을 이용해 진행 방향을, 매개변수 기법을 통해 얻은 보폭으로 거리를 업데이트해 줌으로써 보행 중 발생하는 여러 동작에서도 강인하게 운용할 수 있다. 다양한 동작 상황 및 경로를 고려한 실험을 통해 위에서 제안한 방법이 다양한 실내 환경에서도 안정적으로 위치를 추정하고 성능이 향상됨을 확인하였다.Chapter 1 Introduction 1 1.1 Motivation and Background 1 1.2 Objectives and Contribution 5 1.3 Organization of the Dissertation 6 Chapter 2 Pedestrian Dead Reckoning System 8 2.1 Overview of Pedestrian Dead Reckoning 8 2.2 Parametric Approach 9 2.2.1 Step detection algorithm 11 2.2.2 Step length estimation algorithm 13 2.2.3 Heading estimation 14 2.3 Integration Approach 15 2.3.1 Extended Kalman filter 16 2.3.2 INS-EKF-ZUPT 19 2.4 Activity Recognition using Machine Learning 21 2.4.1 Challenges in HAR 21 2.4.2 Activity recognition chain 22 Chapter 3 Attitude Estimation in Smartphone 26 3.1 Adaptive Attitude Estimation in Smartphone 26 3.1.1 Indirect Kalman filter-based attitude estimation 26 3.1.2 Conventional attitude estimation algorithms 29 3.1.3 Adaptive attitude estimation using ellipsoidal methods 30 3.2 Experimental Results 36 3.2.1 Simulation 36 3.2.2 Rate table experiment 44 3.2.3 Handheld rotation experiment 46 3.2.4 Magnetic disturbance experiment 49 3.3 Summary 53 Chapter 4 Pedestrian Dead Reckoning in Multiple Poses of a Smartphone 54 4.1 System Overview 55 4.2 Machine Learning-based Pose Classification 56 4.2.1 Training dataset 57 4.2.2 Feature extraction and selection 58 4.2.3 Pose classification result using supervised learning in PDR 62 4.3 Fusion of the Integration and Parametric Approaches in PDR 65 4.3.1 System model 67 4.3.2 Measurement model 67 4.3.3 Mode selection 74 4.3.4 Observability analysis 76 4.4 Experimental Results 82 4.4.1 AHRS results 82 4.4.2 PCA results 84 4.4.3 IA-PA results 88 4.5 Summary 100 Chapter 5 Conclusions 103 5.1 Summary of the Contributions 103 5.2 Future Works 105 국문초록 125 Acknowledgements 127Docto

Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approachesComment: 22 pages, 13 figure

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system