Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe

Aerial Simultaneous Localization and Mapping Using Earth\u27s Magnetic Anomaly Field

Aerial magnetic navigation has been shown to be a viable GPS-alternative, but requires a prior-surveyed magnetic map. The miniaturization of atomic magnetometers extends their application to small aircraft at low altitudes where magnetic maps are especially inaccurate or unavailable. This research presents a simultaneous localization and mapping (SLAM) approach to constrain the drift of an inertial navigation system (INS) without the need for a magnetic map. The filter was demonstrated using real measurements on a professional survey flight, and on an AFIT unmanned aerial vehicle

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

Laitteiden välisen yhteistyön soveltuvuus älypuhelimilla toteutettavaan sisätilapaikannukseen

A reliable indoor positioning service for smartphones is a service that is often requested. There are several competing technologies already available but a lot of basic research is still done on the subject. This thesis studies the applicability and technological possibilities of improving the performance of a positioning service using peer to peer collaboration. The Bluetooth low energy technology (BLE) offers a possibility to use peer to peer radio signal measurements with smartphones. This could be used to improve the performance of existing positioning algorithms if enough service users are in close proximity to each other. In this thesis a pedestrian simulation system was implemented to study the probability that two positioning service users are in close enough proximity to each other for BLE usage. The suitability of BLE as the collaboration technology was studied by implementing a particle filter based positioning system that uses BLE measurements to track a smartphone. Finally the collaborative BLE system was integrated on top of an existing geomagnetic tracking algorithm and the effect on the positioning performance was studied. It was concluded that the BLE as a technology is suitable for positioning use despite the large measurement uncertainty. BLE based collaboration is feasible in improving the positioning results provided that the basic positioning technology is reliable enough. The pedestrian simulations concluded that with realistic expected number of users in one building most sessions would not benefit from collaboration but it would still likely happen frequently.Luotettava sisätilapaikannuspalvelu on haluttu ominaisuus mobiilipalveluiden kehityksessä. Useita kilpailevia ratkaisuja on jo markkinoilla, mutta ongelman parissa tehdään vielä huomattavan paljon perustutkimusta. Tässä diplomityössä tutkitaan mahdollisuutta parantaa paikannusjärjestelmän toimintaa käyttäen vertaisyhteistyötä. Bluetooth low energy -teknologia (BLE) tarjoaa mahdollisuuden käyttää laitteiden välisiä radiosignaalimittauksia älypuhelimilla. Tätä voidaan mahdollisesti hyödyntää parantamaan olemassa olevien paikannusalgoritmien toimintaa, jos riittävästi käyttäjiä on riittävän lähellä toisiaan. Tässä diplomityössä toteutettiin ihmisjoukkojen liikettä sisätiloissa mallintava järjestelmä, jolla tutkittiin todennäköisyyttä, että kaksi paikannusjärjestelmän käyttäjää olisi riittävän lähellä toisiaan käyttääkseen BLE-radiomittauksia. BLE:n soveltuvuutta paikannusteknologiana tutkittiin toteuttamalla partikkelisuotimeen perustuva paikannusjärjestelmä, joka käyttää BLE-mittauksia älypuhelimen seuraamiseen. Lopuksi BLE mittausjärjestelmä integroitiin olemassa olevaan magneettikenttään perustuvaan paikannusalgoritmiin ja BLE-yhteistyön vaikutusta algoritmin toimintaan tutkittiin. Työ osoitti, että BLE on paikannuskäyttöön soveltuva teknologia suuresta mittausepävarmuudesta huolimatta. BLE-perusteinen yhteistyö paikannustuloksen parantamisessa on toimiva ratkaisu, mikäli varsinainen paikannusteknologia on riittävän luotettava. Realistisesti odotettavissa olevilla paikannuspalvelun käyttäjämäärillä BLE-yhteistyötä todennäköisesti tapahtuisi suhteellisen usein, vaikka suurin osa paikannussessioista ei pääsisikään hyötymään siitä

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements

Recognition of activities of daily living

Activities of daily living (ADL) are things we normally do in daily living, including any daily activity such as feeding ourselves, bathing, dressing, grooming, work, homemaking, and leisure. The ability or inability to perform ADLs can be used as a very practical measure of human capability in many types of disorder and disability. Oftentimes in a health care facility, with the help of observations by nurses and self-reporting by residents, professional staff manually collect ADL data and enter data into the system. Technologies in smart homes can provide some solutions to detecting and monitoring a resident’s ADL. Typically multiple sensors can be deployed, such as surveillance cameras in the smart home environment, and contacted sensors affixed to the resident’s body. Note that the traditional technologies incur costly and laborious sensor deployment, and cause uncomfortable feeling of contacted sensors with increased inconvenience. This work presents a novel system facilitated via mobile devices to collect and analyze mobile data pertaining to the human users’ ADL. By employing only one smart phone, this system, named ADL recognition system, significantly reduces set-up costs and saves manpower. It encapsulates rather sophisticated technologies under the hood, such as an agent-based information management platform integrating both the mobile end and the cloud, observer patterns and a time-series based motion analysis mechanism over sensory data. As a single-point deployment system, ADL recognition system provides further benefits that enable the replay of users’ daily ADL routines, in addition to the timely assessment of their life habits