Mobility increases localizability: A survey on wireless indoor localization using inertial sensors

Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.</jats:p

Multi sensor system for pedestrian tracking and activity recognition in indoor environments

The widespread use of mobile devices and the rise of Global Navigation Satellite Systems (GNSS) have allowed mobile tracking applications to become very popular and valuable in outdoor environments. However, tracking pedestrians in indoor environments with Global Positioning System (GPS)-based schemes is still very challenging. Along with indoor tracking, the ability to recognize pedestrian behavior and activities can lead to considerable growth in location-based applications including pervasive healthcare, leisure and guide services (such as, hospitals, museums, airports, etc.), and emergency services, among the most important ones. This paper presents a system for pedestrian tracking and activity recognition in indoor environments using exclusively common off-the-shelf sensors embedded in smartphones (accelerometer, gyroscope, magnetometer and barometer). The proposed system combines the knowledge found in biomechanical patterns of the human body while accomplishing basic activities, such as walking or climbing stairs up and down, along with identifiable signatures that certain indoor locations (such as turns or elevators) introduce on sensing data. The system was implemented and tested on Android-based mobile phones. The system detects and counts steps with an accuracy of 97% and 96:67% in flat floor and stairs, respectively; detects user changes of direction and altitude with 98:88% and 96:66% accuracy, respectively; and recognizes the proposed human activities with a 95% accuracy. All modules combined lead to a total tracking accuracy of 91:06% in common human motion indoor displacement

Indoor pedestrian dead reckoning calibration by visual tracking and map information

Currently, Pedestrian Dead Reckoning (PDR) systems are becoming more attractive in market of indoor positioning. This is mainly due to the development of cheap and light Micro Electro-Mechanical Systems (MEMS) on smartphones and less requirement of additional infrastructures in indoor areas. However, it still faces the problem of drift accumulation and needs the support from external positioning systems. Vision-aided inertial navigation, as one possible solution to that problem, has become very popular in indoor localization with satisfied performance than individual PDR system. In the literature however, previous studies use fixed platform and the visual tracking uses feature-extraction-based methods. This paper instead contributes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical system can only provide relative positioning information, this paper contributes a method to integrate digital map with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones as iOS and Android, based on corresponded data collection apps respectively, in order to test the robustness of method. It also uses two different ways for calibration, by time synchronization of positions and heading calibration based on time steps. According to the results, localization information collected from both operation systems has been significantly improved after integrating with visual tracking data

SLAM for Visually Impaired People: A Survey

In recent decades, several assistive technologies for visually impaired and blind (VIB) people have been developed to improve their ability to navigate independently and safely. At the same time, simultaneous localization and mapping (SLAM) techniques have become sufficiently robust and efficient to be adopted in the development of assistive technologies. In this paper, we first report the results of an anonymous survey conducted with VIB people to understand their experience and needs; we focus on digital assistive technologies that help them with indoor and outdoor navigation. Then, we present a literature review of assistive technologies based on SLAM. We discuss proposed approaches and indicate their pros and cons. We conclude by presenting future opportunities and challenges in this domain.Comment: 26 pages, 5 tables, 3 figure

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

보행자 항법에서 계단 보행 시 진행 방향 신호의 형상 분석을 통한 층 결정 알고리즘

학위논문(석사) -- 서울대학교대학원 : 공과대학 항공우주공학과, 2022.2. 박찬국.This master’s thesis presents a new algorithm for determining floors in pedestrian navigation. In the proposed algorithm, the types of stairs are classified by shape analysis, and the floors are determined based on the stair type. In order to implement our algorithm, the walking direction estimated through the Pedestrian Dead Reckoning (PDR) system is used. The walking direction signal has different shapes depending on the stair types. Then, shape analysis is applied to the signal shapes of the walking direction to identify the types of stairs and determine the floor change. The proposed algorithm is verified through simulations and experiments, and it is confirmed that it works well even when moving through multiple floors with several different types of stairs. It is also verified that the performance is superior to the conventional floor determination algorithm.본 논문에서는 관성 측정 장치(IMU: Inertial Measurement Unit)를 이용한 실내 보행자 항법에서 계단을 통한 층 이동 시 계단의 종류를 파악하여 층을 결정하는 새로운 알고리즘을 제안한다. 이를 위해 보행자항법(PDR: Pedestrian Dead Reckoning) 시스템에서 추정된 고도, 걸음 검출 시간, 그리고 방향각을 사용한다. 이때 추정된 고도는 계단 보행을 시작하거나 마칠 때 평지 보행과 구분될 정도의 정확도만 필요하다. 따라서 본 알고리즘에서는 기존의 층 구분 알고리즘에서 필요로 하는 고도 추정치에 대한 의존성을 최소화할 수 있다. 제안한 알고리즘에서는 계단 보행 시에 나타나는 방향각의 신호에 통계적 형상 분석(statistical shape analysis) 기법을 적용하여 계단의 종류를 파악한 후 층을 구분하게 된다. 시뮬레이션과 실험을 통해 제안한 알고리즘의 정확도를 검증하며 여러 종류의 계단을 여러 층 오르내리는 경우에도 알고리즘이 잘 동작함을 확인한다. 그리고 기존의 층 구분 알고리즘에서 발생하는 시간 지연 문제를 해결하고 층 구분 정확도가 높아진 것을 확인한다. 또한 본 연구는 관성 측정장치 이외의 다른 센서나 무선통신 장치를 사용하지 않으며 층 높이와 같은 건물에 대한 기본 정보를 가정하지 않고도 층을 잘 결정할 수 있다는 유효성을 가진다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Objectives and Contributions 2 Chapter 2 Pedestrian Dead Reckoning System 4 2.1 Overview of Pedestrian Dead Reckoning 4 2.2 Integration Approach 5 2.2.1 Strapdown inertial navigation system 5 2.2.2 Extended Kalman filter 6 2.2.3 INS-EKF-ZUPT 7 Chapter 3 Shape Analysis 10 3.1 Euclidean Similarity Transformation 11 3.2 Full Procrustes Distance 12 Chapter 4 Floor Determination 14 4.1 Stair Types 15 4.2 Stair Type Classification Algorithm 17 4.3 Floor Determination Algorithm 18 Chapter 5 Simulation and Experimental Results 22 5.1 Simulation Results 22 5.2 Experimental Results Single Floor Change 30 5.3 Experimental Results Multiple Floor Changes 32 5.3.1 Scenario 1 32 5.3.2 Scenario 2 37 Chapter 6 Conclusion 40 6.1 Conclusion and Summary 40 6.2 Future Work 41 Bibliography 42 국문초록 46석