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

Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization

Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

CrowdFusion: Multi-Signal Fusion SLAM Positioning Leveraging Visible Light

With the fast development of location-based services, an ubiquitous indoor positioning approach with high accuracy and low calibration has become increasingly important. In this work, we target on a crowdsourcing approach with zero calibration effort based on visible light, magnetic field and WiFi to achieve sub-meter accuracy. We propose a CrowdFusion Simultaneous Localization and Mapping (SLAM) comprised of coarse-grained and fine-grained trace merging respectively based on the Iterative Closest Point (ICP) SLAM and GraphSLAM. ICP SLAM is proposed to correct the relative locations and directions of crowdsourcing traces and GraphSLAM is further adopted for fine-grained pose optimization. In CrowdFusion SLAM, visible light is used to accurately detect loop closures and magnetic field to extend the coverage. According to the merged traces, we construct a radio map with visible light and WiFi fingerprints. An enhanced particle filter fusing inertial sensors, visible light, WiFi and floor plan is designed, in which visible light fingerprinting is used to improve the accuracy and increase the resampling/rebooting efficiency. We evaluate CrowdFusion based on comprehensive experiments. The evaluation results show a mean accuracy of 0.67m for the merged traces and 0.77m for positioning, merely replying on crowdsourcing traces without professional calibration

Floor plan-free particle filter for indoor positioning of industrial vehicles

Industry 4.0 is triggering the rapid development of solutions for indoor localization of industrial ve- hicles in the factories of the future. Either to support indoor navigation or to improve the operations of the factory, the localization of industrial vehicles imposes demanding requirements such as high accuracy, coverage of the entire operating area, low convergence time and high reliability. Industrial vehicles can be located using Wi-Fi fingerprinting, although with large positioning errors. In addition, these vehicles may be tracked with motion sensors, however an initial position is necessary and these sensors often suffer from cumulative errors (e.g. drift in the heading). To overcome these problems, we propose an indoor positioning system (IPS) based on a particle filter that combines Wi-Fi fingerprinting with data from motion sensors (displacement and heading). Wi-Fi position estimates are obtained using a novel approach, which explores signal strength measurements from multiple Wi-Fi interfaces. This IPS is capable of locating a vehicle prototype without prior knowledge of the starting position and heading, without depending on the building’s floor plan. An average positioning error of 0.74 m was achieved in performed tests in a factory-like building.FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, the PhD fellowship PD/BD/137401/2018 and the Technological Development in the scope of the projects in co-promotion no 002814/2015 (iFACTORY 2015-2018

지자기 지문을 이용한 비용 효율적이고 실용적인 실내 측위 시스템

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 권태경.지난 십수 년 동안 학계와 산업 영역을 막론하여 실내 측위 시스템이 널리 연구되어 왔다. 실내 측위 시스템을 설계할 때 WiFi, Bluetooth, 관성 센서와 같은 다양한 종류의 센서나 무선 인터페이스들을 활용할 수 있는데, 그 중에서도 지자기 센서에서 측정한 자기장의 패턴을 측위에 사용하는 시스템은 정확성과 안정성 측면에서 타 시스템에 비해 큰 장점을 지니고 있다. 철골 구조에 기반하여 건축된 현대 건축물들의 실내 공간에는 지자기장의 왜곡이 발생하며 이는 곧 실내의 개별 공간들에 고유하고 안정적인 지자기 패턴을 발생시킨다. 이를 실내 측위의 맥락에서는 지자기 지문이라고 정의하는데 이 지자기 지문은 사용자의 움직임, 문과 창문의 여닫힘 등과 같은 일상적인 환경 변화에 강건하며, 특히 WiFi 등 무선 신호와 비교하였을 때 높은 수준의 안정성을 보인다. 그러나 이와 같은 안정성에도 불구하고 지자기 지문을 이용하여 실내 측위 시스템을 설계할 때에는 몇가지 고려해야할 사항들이 있다. 먼저 지자기 지문은 센서 데이터의 낮은 차원 개수로 인하여 AP 개수에 따라 데이터의 차원이 수십, 수백개에 달하는 무선 신호에 비하여 구별성이 매우 낮다. 대부분의 기존 연구들은 많은 양의 연산을 수행하는 복잡한 알고리즘과 많은 센서를 이용하여 이를 극복하였다. 또 대상 공간의 지자기 지문을 수집하기 위한 사전 조사 비용과 주기적인 지자기 지문 재수집에 드는 관리 비용 역시 지자기 지문을 사용할 때 고려해야할 문제점이다. 따라서 본 논문은 이 두 가지 문제점을 해결하는 데에 초점을 맞추어 작성되었다. 먼저 사물 인터넷 (IoT, Internet of Things) 환경에서 실내 측위를 위해 지자기 지문을 활용하는 에너지 효율적인 경량 시스템을 연구하였다. BLE 인터페이스와 센서 2개(지자기 및 가속도계)만 탑재한 새로운 하드웨어 설계 방식을 제안하였으며, 이 기기는 코인 크기의 배터리를 사용할 경우 1년 동안 동작 가능하였다. 또한 최소한의 센서 데이터만을 이용하여 강건한 사용자 보행 모델과 효율적인 알고리즘을 도입한 파티클 필터 프레임워크를 제안하였다. 직접 설계한 사물인터넷 기기와 알고리즘을 적용한 결과, 본 시스템은 낮은 계산 복잡성과 높은 에너지 효율을 보여주면서 일반적인 사무실 공간에 대하여 평균 1.62m의 측위 정확도를 달성하였다. 다음으로는 크라우드소싱 방식을 활용하는 지자기 지문 기반의 실내 측위 시스템을 제안하였다. 실내 측위의 맥락에서 크라우드소싱은 명시적인 대상 공간의 사전 조사 과정 없이 지자기 지문 데이터베이스를 구성하는 방법이다. 지난 십수년 간 실내 측위 연구를 위해 크라우드소싱 방법론이 활발하게 연구되어 왔으나, 크라우드소싱에 기반한 기존 측위 시스템은 일반적으로 사전 조사 기반 시스템보다 위치 정확도가 낮게 측정되어왔다. 크라우드소싱 기반 시스텝의 낮은 측위 성능을 극복하기 위해, 본 연구에서는 지자기 기반 크라우드소싱 데이터를 사용한 실내 측위 시스템을 제안한다. 명시적인 수집 과정 없이 스마트폰 사용자가 일상생활에서 자연스럽게 지자기 지문 데이터베이스를 구축할 수 있도록 HMM 기반의 새로운 학습 모델을 구현하였으며, 주로 복도 위주로 구성된 실내 공간에서의 평가 결과, 제안된 시스템이 96.47\%의 학습 정확도와 0.25m의 중간값 측위 정확도를 달성하였다.Over the decades, indoor localization system has been widely studied in the academic and also in the industrial area. Many sensors or wireless signals such as WiFi, Bluetooth, and inertial sensors are available when designing an indoor localization system, but among them, the systems using the geomagnetic field has advantages concerning accuracy and stability. Every spatial point in an indoor space has its own distinct and stable fingerprint, which arises owing to the distortion of the magnetic field induced by the surrounding steel and iron structures. The magnetic fingerprint is robust to environmental changes like pedestrian activities and door/window movements, particularly compared with radio signals such as WiFi. This phenomenon makes many indoor positioning techniques rely on the magnetic field as an essential source of localization. Despite the robustness, there are some challenges when leveraging the magnetic fingerprint to design the indoor localization system. Due to lower discernibility of the magnetic fingerprint, most of the existing studies have exploited high computational algorithms and many sensors. Also, the cost of a site survey to collect the fingerprints and periodic management of target spaces is still problematic when using magnetic fingerprints. This dissertation thus focuses on these two challenges. First, we present an energy-efficient and lightweight system that utilizes the magnetic field for indoor positioning in the Internet of Things (IoT) environments. We propose a new hardware design of an IoT device that only has a BLE interface and two sensors (magnetometer and accelerometer), with the lifetime of one year when using a coin-size battery. We further propose an augmented particle filter framework that features a robust motion model and algorithmic-efficient localization heuristics with minimal sensory data. The prototype-based evaluation shows that the proposed system achieves a median accuracy of 1.62 m for an office building while exhibiting low computational complexity and high energy efficiency. Next, we propose a magnetic fingerprint-based indoor localization system leveraging a crowdsourcing approach. In the aspect of indoor localization, crowdsourcing is a method to construct the fingerprint database without the explicit site-survey process. Over the past decade, crowdsourcing has been actively studied for indoor localization. However, the existing localization systems based on crowdsourcing usually achieve lower location accuracy than the site survey based systems. To overcome the low performance of the crowdsourcing based approaches, we design an indoor positioning system using the crowdsourced data of the magnetic field. We substantiate a novel HMM-based learning model to construct a database of magnetic field fingerprints from smartphone users. Experiments in an indoor space consisting of aisles show that the proposed system achieves the learning accuracy of 96.47\% and median positioning accuracy of 0.25m.Chapter 1 Introduction 1p - Motivation 1p - Indoor Localization Overview 2p - Magnetic Field based Systems 5p - Organization of Dissertation 7p Chapter 2 An Energy-efficient and Lightweight Indoor Localization System for Internet-of-Things (IoT) Environments 8p - Introduction 8p - Related Work 11p - Issues on Using Magnetic Field 12p - Characteristics of Magnetic Field 12p - Variation Issues 13p - Sensing Rate 17p - Design of Energy-efficient Device for Localization 18p - Hardware Design 18p - Structure of the BLE Beacon Frame 21p - Processing Sensor Data 22p - System Architecture 25p - Overview 25p - Site Survey Methodology 25p - Particle Filter Framework 26p - Evaluation 36p - Implementation 37p - Experiment Setup 38p - Localization Performance 38p - Energy and Algorithmic Efficiency 44p - Discussion 46p Chapter 3 Magnetic Field based Indoor Localization System:A Crowdsourcing Approach 51p - Introduction 51p - Characteristics of Magnetic Field 54p - Robustness 54p - Distinctness 54p - Diversity issues 55p - Design of HMM for Crowdsoucing 56p - Basic Model 56p - Issues in HMM Learning 59p - Preliminary Experiments 59p - System Architecture 63p - Enhanced Learning Model 63p - Pre-processing Crowdsourced Data 65p - Allocating Initial HMM Parameters 67p - Comparing Similarity between the Magnetic Fingerprints 70p - Evaluation 71p - Experimental Settings 71p - Learning Accuracy 71p - Positioning Accuracy 73p - Algorithmic Efficiency 75p Chapter 4 Discussion and Future Work 76p - Open Space Issue 76p Chapter 5 Conclusion 82p Bibliography 84p 초록 92pDocto