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 (75 th 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

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

The IPIN 2019 Indoor Localisation Competition—Description and Results

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks

Autonomous localization by learning mobility dynamics in multimodal transportation

Le développement croissant d'objets intelligents offre de nouvelles opportunités de localisation du voyageur connecté. Cependant, le suivi de la trajectoire du piéton reste problématique et les applications de navigation ne proposent pas de suivre la trajectoire du voyageur à l’échelle multimodale de façon autonome. Ce travail s’intéresse à la mise en place d’une solution unique capable de localiser l’utilisateur selon différents mode de déplacement et quel que soit l’environnement, à partir de capteurs inertiels, magnétique et GNSS. Dans un premier temps, une nouvelle méthode de localisation du cycliste est mise en place. Les mesures de phases GNSS sont utilisées pour corriger le vecteur vitesse par différences temporelles et la direction de déplacement est contrainte à l'aide des signaux inertiels. Ces éléments ont été utilisés dans un second temps et adaptés pour mettre en place une nouvelle méthode de localisation du piéton avec un capteur en main. L’approche PDR qui est une technique de navigation inertielle à l’estime est paramétrée dans un filtre de Kalman étendu. Une mise à jour innovante fusionnant l’estimation de l’attitude du boîtier et une estimation statistique de la direction de marche permet de corriger l’estimation du cap de marche et obtenir une estimation cohérente et lissée. Les mesures GNSS sont utilisées pour corriger le vecteur vitesse, l’orientation, la longueur de pas et la position absolue. Enfin, une approche multimodale est proposée et la gestion des transitions entre les différents algorithmes, assistée par l’utilisation d’un capteur innovant, est étudiée. Des validations expérimentales multimodales en conditions réelles sont conduites pour analyser les performances d’estimation de la solution proposée.The growing development of smart objects offers new opportunities for locating the connected traveller. However, tracking the trajectory of the pedestrian remains problematic and navigation applications do not offer to track the traveller's trajectory on a multimodal level in an autonomous way. This work focuses on the implementation of a single solution able to locate the user according to different travel modes and whatever the environment, using inertial, magnetic and GNSS sensors. In a first step, a new method for locating the cyclist is implemented. GNSS phase measurements are used to correct the velocity vector by time differences and the motion direction is constrained using inertial signals. These elements were used in a second step and adapted to implement a new method of pedestrian localization with a handheld sensor. The PDR approach, which is an inertial dead reckoning navigation technique, is parameterized in an extended Kalman filter. An innovative update merging the device attitude estimation and a statistical estimation of the walking direction allows to correct the walking heading estimation and obtain a consistent and smoothed estimation. GNSS measurements are used to correct speed vector, orientation, step length and absolute position. Finally, a multimodal approach is proposed and the management of transitions between the different algorithms, assisted by the use of an innovative sensor, is studied. Multimodal experimental validations in real conditions are conducted to analyze the estimation performances of the proposed solution

Localisation autonome par apprentissage des dynamiques de déplacement en transport multimodal

The growing development of smart objects offers new opportunities for locating the connected traveller. However, tracking the trajectory of the pedestrian remains problematic and navigation applications do not offer to track the traveller's trajectory on a multimodal level in an autonomous way. This work focuses on the implementation of a single solution able to locate the user according to different travel modes and whatever the environment, using inertial, magnetic and GNSS sensors. In a first step, a new method for locating the cyclist is implemented. GNSS phase measurements are used to correct the velocity vector by time differences and the motion direction is constrained using inertial signals. These elements were used in a second step and adapted to implement a new method of pedestrian localization with a handheld sensor. The PDR approach, which is an inertial dead reckoning navigation technique, is parameterized in an extended Kalman filter. An innovative update merging the device attitude estimation and a statistical estimation of the walking direction allows to correct the walking heading estimation and obtain a consistent and smoothed estimation. GNSS measurements are used to correct speed vector, orientation, step length and absolute position. Finally, a multimodal approach is proposed and the management of transitions between the different algorithms, assisted by the use of an innovative sensor, is studied. Multimodal experimental validations in real conditions are conducted to analyze the estimation performances of the proposed solution.Le développement croissant d'objets intelligents offre de nouvelles opportunités de localisation du voyageur connecté. Cependant, le suivi de la trajectoire du piéton reste problématique et les applications de navigation ne proposent pas de suivre la trajectoire du voyageur à l’échelle multimodale de façon autonome. Ce travail s’intéresse à la mise en place d’une solution unique capable de localiser l’utilisateur selon différents mode de déplacement et quel que soit l’environnement, à partir de capteurs inertiels, magnétique et GNSS. Dans un premier temps, une nouvelle méthode de localisation du cycliste est mise en place. Les mesures de phases GNSS sont utilisées pour corriger le vecteur vitesse par différences temporelles et la direction de déplacement est contrainte à l'aide des signaux inertiels. Ces éléments ont été utilisés dans un second temps et adaptés pour mettre en place une nouvelle méthode de localisation du piéton avec un capteur en main. L’approche PDR qui est une technique de navigation inertielle à l’estime est paramétrée dans un filtre de Kalman étendu. Une mise à jour innovante fusionnant l’estimation de l’attitude du boîtier et une estimation statistique de la direction de marche permet de corriger l’estimation du cap de marche et obtenir une estimation cohérente et lissée. Les mesures GNSS sont utilisées pour corriger le vecteur vitesse, l’orientation, la longueur de pas et la position absolue. Enfin, une approche multimodale est proposée et la gestion des transitions entre les différents algorithmes, assistée par l’utilisation d’un capteur innovant, est étudiée. Des validations expérimentales multimodales en conditions réelles sont conduites pour analyser les performances d’estimation de la solution proposée

Fusion of Attitude and Statistical Walking Direction Estimations with Time-Difference Carrier Phase Velocity Update for Pedestrian Dead Reckoning Method

32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), MIAMI, ETATS-UNIS, 16-/09/2019 - 20/09/2019Pedestrian location in urban or indoor environments is particularly complex. Indeed, GNSS technology generally used for localization is no longer sufficient in these challenging environments. However, the presence of many sensors in consumer equipment like smartphones allows the implementation of different methods. PDR (Pedestrian Dead Reckoning) is a position estimation method using inertial and magnetic sensor data. It is based on the determination of two elements: the step length and the walking direction. This direction is difficult to estimate for handheld sensors because the orientation of the sensor is not always aligned with the walking direction. Methods based on the study of horizontal hand accelerations can overcome this difficulty, but performance on real scenarios is not sufficient. This article proposes a new method for estimating the walking direction and position based on an extended Kalman filter. For this purpose, the angular estimates from the WAISS and MAGYQ algorithms are merged to update the estimate of the walking direction. Phase measurements are used with TDCP updates to correct the velocity and correct the walking direction. 6 experiments carried out with three subjects over distances between 650 and 1300m in texting mode, in real and challenging conditions are conducted. The mean angular error obtained is 4.6° and the mean position error is 0.5% of the travelled distanc

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 (75 th 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

Urban Vulnerable Road User Localization using GNSS, Inertial Sensors and Ultra-Wideband Ranging

Over the last decade, the number of accidents involving Vulnerable Road Users (VRU), i.e. pedestrians, cyclists and motorbike drivers, has not decreased in the same way as accidents between passenger cars have. Cooperative systems based on Vehicle-to-X (V2X) communication make it possible to directly exchange information between VRUs and vehicles and to increase the overall situational awareness beyond the capabilities of on-board ranging sensors. To detect and avoid collisions, vehicles require up-to-date and precise information on the location and trajectory of VRUs. In this paper, we propose a VRU localization system based on Global Navigation Satellite System (GNSS), inertial sensors and ultra-wideband (UWB) round-trip-delay ranging technology. We present an exhaustive measurement campaign comprising pedestrians, cyclists and vehicles performed in an urban setting and show first results on the localization performance for a pedestrian crossing an intersection. In the experiments, the pedestrian inertial system supported by GNSS and UWB ranges is able to achieve 0.65m 1\sigma-position accuracy

Préparation de la compétition internationale de localisation intérieure IPIN : cartographie de parcours piétons

Depuis quelques années des compétitions internationales destinées à comparer les technologies de localisation à l'intérieur des bâtiments sont organisées. Face à la diversification de ces technologies, elles permettent de fixer une cadre unique d'évaluation des performances de localisation en temps réel ou différé. Un levé topographique d'envergure qui combine mesures au théodolite, par GNSS différentiel et scanner 3D a permis de cartographier à 10 cm près les 180 cibles réparties dans le centre commercial Atlantis à Nantes. Ces cibles définissent les parcours sur lesquels les compétiteurs du congrès international IPIN s'affronteront le 22 septembre. Ce projet a été réalisé par quatre étudiants de l'ESGT sous la direction du laboratoire GEOLOC de l'IFSTTAR et avec le soutien de la société Viametris