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

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

Indoor positioning for smartphones without infrastructure and user adaptable

Given that the classic solutions for positioning outdoors, such as GPS (Global Positioning System) or GNSS (Global Navigation Satellite System) do not work indoors, there have been emerging multiple alternatives for Indoor Location. Usually these solutions require extensive and complex installations, which involve high costs. In this thesis we present a robust indoor positioning solution for smartphones that maximizes location accuracy while minimizes the required infrastructure. We have considered two main modes of displacement: walking and in a vehicle. Our solution is robust to different users, allows them to carry the phone in different positions and allows to use the device freely while performing different daily activities, such as walking, driving , going up and down stairs, etc. We achieved that by developing a robust indoor positioning system that combines information from multiple sources such as radio frequency readings and inertial sensors

The smartphone-based offline indoor location competition at IPIN 2016: analysis and future work

This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors' estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.We would like to thank Tecnalia Research & Innovation Foundation for sponsoring the competition track with an award for the winning team. We are also grateful to Francesco Potortì, Sangjoon Park, Jesús Ureña and Kyle O’Keefe for their invaluable help in promoting the IPIN competition and conference. Parts of this work was carried out with the financial support received from projects and grants: LORIS (TIN2012-38080-C04-04), TARSIUS (TIN2015-71564-C4-2-R (MINECO/FEDER)), SmartLoc (CSIC-PIE Ref.201450E011), “Metodologías avanzadas para el diseño, desarrollo, evaluación e integración de algoritmos de localización en interiores” (TIN2015-70202-P), REPNIN network (TEC2015-71426-REDT) and the José Castillejo mobility grant (CAS16/00072). The HFTS team has been supported in the frame of the German Federal Ministry of Education and Research programme “FHprofUnt2013” under contract 03FH035PB3 (Project SPIRIT). The UMinho team has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT — Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

A review of smartphones based indoor positioning: challenges and applications

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients

iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

As a key technology that is widely adopted in location-based services (LBS), indoor localization has received considerable attention in both research and industrial areas. Despite the huge efforts made for localization using smartphone inertial sensors, its performance is still unsatisfactory in large open areas, such as halls, supermarkets, and museums, due to accumulated errors arising from the uncertainty of users’ mobility and fluctuations of magnetic field. Regarding that, this paper presents iBILL, an indoor localization approach that jointly uses iBeacon and inertial sensors in large open areas. With users’ real-time locations estimated by inertial sensors through an improved particle filter, we revise the algorithm of augmented particle filter to cope with fluctuations of magnetic field. When users enter vicinity of iBeacon devices clusters, their locations are accurately determined based on received signal strength of iBeacon devices, and accumulated errors can, therefore, be corrected. Proposed by Apple Inc. for developing LBS market, iBeacon is a type of Bluetooth low energy, and we characterize both the advantages and limitations of localization when it is utilized. Moreover, with the help of iBeacon devices, we also provide solutions of two localization problems that have long remained tough due to the increasingly large computational overhead and arbitrarily placed smartphones. Through extensive experiments in the library on our campus, we demonstrate that iBILL exhibits 90% errors within 3.5 m in large open areas