Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Navigation Using Inertial Sensors

This tutorial provides an introduction to navigation using inertial sensors, explaining the underlying principles. Topics covered include accelerometer and gyro technology and their characteristics, strapdown inertial navigation, attitude determination, integration and alignment, zero updates, motion constraints, pedestrian dead reckoning using step detection, and fault detection

Use of an inertial/magnetic sensor module for pedestrian tracking during normal walking

The ability to track pedestrians without any infrastructure support is required by numerous applications in the healthcare, augmented reality, and entertainment industries. In this paper, we present a simple self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module. Traditional methods normally incorporate double integration of the measured acceleration, but such methods are susceptible to the acceleration noise and integration drift. To avoid this issue, alternative approaches which make use of walking dynamics to aggregate individual stride have been explored. The key for stride aggregating is to accurately and reliably detect stride boundary and estimate the associated heading direction for each stride, but it is still not well solved yet due to sensor noise and external disturbance. In this paper, we propose to make use of the inertial sensor and magnetometer measurements for stride detection and heading direction determination. In our method, a simple and reliable stride detection method, which is resilient to random bouncing motions and sensor noise, is designed based on gyroscope and accelerometer measurements. Heading direction is then determined from the foot's orientation which fuses all the three types of sensor information together. The proposed pedestrian tracking method has been evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors. The good experimental results have illustrated the effectiveness of the proposed pedestrian tracking method

사용자 상황인지 딥러닝을 사용한 GPS 반송파 / 관성 센서 결합 스마트폰 보행자 항법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 여재익.본 논문에서는 스마트폰 Galaxy S8 환경에서 GPS / INS 결합 보행자 항법을 수행하였으며, 스마트폰 센서의 특성을 자세히 분석하였다. 이에 최근 공개된 Android GNSS API 를 사용하여 GPS 원시데이터를 항법에 이용하면서, Cycle slip 을 보정한 Carrier phase 를 이용한 속도 결정법을 사용하였다. 이로 인해 기존의 NMEA GPS 를 사용한 방식의 스마트폰 보행자 항법보다 정밀한 위치, 속도 항법이 가능하였고, 성능을 향상 시켰다. 또한 사용자 상황 분석이 가능한 분류 딥러닝 기법을 사용하여 각 보행 상황에 따른 분류감지가 가능하였음 보였으며, LSTM 의 입력부분을 변화한 몇가지 딥러닝 모델의 성능을 비교하였다. 이를 통해서 사용자의 보행 상황에 따른 적응적 보행자 항법 파라메터 결정이 가능함의 가능성을 보였다.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

Self-Contained Pedestrian Tracking During Normal Walking Using an Inertial/Magnetic Sensor Module

This paper proposes a novel self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity updates, but also applies the stride information to further correct the acceleration double integration drifts and thus improves the tracking accuracy. In our method, a velocity control variable is designed in the process model, which is set to the average velocity derived from stride information in the swing (nonzero velocity) phases or zero in the stance (zero-velocity) phases. Stride-based position information is also derived as the pseudomeasurements to further improve the accuracy of the position estimates. An adaptive Kalman filter is then designed to fuse all the sensor information and pseudomeasurements. The proposed pedestrian tracking method has been extensively evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors, and have been shown to perform effectively for pedestrian tracking

Fusion of non-visual and visual sensors for human tracking

Human tracking is an extensively researched yet still challenging area in the Computer Vision field, with a wide range of applications such as surveillance and healthcare. People may not be successfully tracked with merely the visual information in challenging cases such as long-term occlusion. Thus, we propose to combine information from other sensors with the surveillance cameras to persistently localize and track humans, which is becoming more promising with the pervasiveness of mobile devices such as cellphones, smart watches and smart glasses embedded with all kinds of sensors including accelerometers, gyroscopes, magnetometers, GPS, WiFi modules and so on. In this thesis, we firstly investigate the application of Inertial Measurement Unit (IMU) from mobile devices to human activity recognition and human tracking, we then develop novel persistent human tracking and indoor localization algorithms by the fusion of non-visual sensors and visual sensors, which not only overcomes the occlusion challenge in visual tracking, but also alleviates the calibration and drift problems in IMU tracking --Abstract, page iii