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Foot-mounted positioning devices are becoming more and more popular in the different application field. For example, inertial sensors are now embedded in safety shoes to monitor security. They allow positioning with zero velocity update to bound the error growth of foot-mounted inertial sensors. High positioning accuracy depends on robust zero velocity detector (ZVD). Existing Artificial Intelligent (AI)-based methods classify the pedestrian dynamics to adjust ZVD at the cost of high computation costs and error propagation from miss-classification. We propose a machine learning model to detect zero velocity moments without any pre-classification step, named Uniform AI Model for All pedestrian Motions (UMAM). Performance is evaluated by benchmarking on two new subjects of opposite gender and different size, not included in the training data set, over complex indoor/outdoor paths of 2 km for subject 1 and 2.1 km for subject 2. We obtain an average 2D loop closure error of less than 0.37%.

Global Navigation Satellite System (GNSS) integrity is defined as a measure of trust that can be placed in the correctness of the information supplied by the total system. Initially developed for safety critical applications in the aeronautic domain, this concept has attracted more and more attention from terrestrial GNSS-based applications in recent years. The main problem of integrity monitoring for urban transport applications is related to GNSS signal degradation due to local effects such as multipath and non-line-of-sight reception. This article proposes two integrity monitoring schemes with two classes of approaches, based on either the snapshot weighted least-squares residual or sequential weighted extended Kalman filter innovation. The integrity monitoring schemes are mainly realized by two modules: accuracy enhancement, in which measurement errors are better characterized, and integrity monitoring, in which one of the fault detection and exclusion techniques, i.e., the Danish reweighting method, is applied. The results with real GPS data collected in urban canyons show that the proposed system can effectively improve positioning accuracy and guarantee system integrity.

ICE Seminar, PARIS, FRANCE, 04-/02/2021 - 04/02/2021; Over the past 15 years, there has been an exponential growth of new technologies for indoor positioning and navigation. Unlike GNSS technology, which has become the leading solution for outdoor positioning, no technology has taken the lead indoors. With the price drop of radio beacons, we see massive deployment of beacons' networks for positioning. Image processing is progressing fast thanks to machine learning techniques that improve the rendering of very low-cost cameras. More and more smart devices embed inertial sensors providing autonomous navigation options. These are only a few of the technologies deployed. Hybridizing technologies to find the best compromise between accuracy, cost and energy consumption is at the heart of ongoing development. The nature of sensors in the infrastructure or smart devices, specific use cases requirements and privacy concerns about geolocated data are all features used to choose the right technologies to hybridize. Adopting a ubiquitous approach that combines dead reckoning and absolute positioning while recognizing the application and environmental context is certainly a strong trend in current developments and research. Given the great diversity of existing positioning systems and ways of presenting their performance, it seems almost impossible to provide a clear comparison of localization performance. The key to this comparison certainly lies in experimental comparative trials, in the same context and on identical scenarios. This approach started several years ago with international positioning competitions. This talk will review main indoor positioning technologies according to several comparison criteria. It will also exploit the results of the 2018 Indoor Positioning Indoor Navigation(IPIN) international competition that took place in a 9'000m² shopping mall (Atlantis) in Nantes (France) where 49 teams competed.

Simultaneous Localization and Mapping is now widely adopted by many applications, and researchers have produced very dense literature on this topic. With the advent of smart devices, embedding cameras, inertial measurement units, visual SLAM (vSLAM), and visual-inertial SLAM (viSLAM) are enabling novel general public applications. In this context, this paper conducts a review of popular SLAM approaches with a focus on vSLAM/viSLAM, both at fundamental and experimental levels. It starts with a structured overview of existing vSLAM and viSLAM designs and continues with a new classification of a dozen main state-of-the-art methods. A chronological survey of viSLAM’s development highlights the historical milestones and presents more recent methods into a classification. Finally, the performance of vSLAM is experimentally assessed for the use case of pedestrian pose estimation with a handheld device in urban environments. The performance of five open-source methods Vins-Mono, ROVIO, ORB-SLAM2, DSO, and LSD-SLAM is compared using the EuRoC MAV dataset and a new visual-inertial dataset corresponding to urban pedestrian navigation. A detailed analysis of the computation results identifies the strengths and weaknesses for each method. Globally, ORB-SLAM2 appears to be the most promising algorithm to address the challenges of urban pedestrian navigation, tested with two datasets.

The use of bicycles is regaining popularity, especially in city centers where they can be used as quickly as cars and reduce carbon footprint. However, in these dense urban environments, navigation methods based on GNSS technologies do not provide sufficient accuracy for cyclist navigation and safety. To mitigate the challenges of indoor-like surroundings, a new positioning algorithm: BIKES (Bicycle Itinerancy Kalman filter with Embedded Sensors) was developed. This extended Kalman filter processes deeply degraded GNSS data to update velocity and position estimates with differential computation approaches working even in degraded environments. GNSS signals are combined with inertial and magnetic data to continuously estimate the trajectory when GNSS is unavailable. BIKES' performance was tested in real-life conditions on a 3 km long path in the city center downtown Nantes and compared to Google Fused Location Provider estimates. A mean positioning error below 1 m with a 0.5 m standard deviation is achieved. These results are 4 times better than the Google solution. This algorithm allows also distinguishing if the cyclist is riding on a bike path, the sidewalk, or the road, which is critical for guidance systems.

3èmes Rencontres Francophones Transport Mobilité, En distanciel, FRANCE, 02-/06/2021 - 04/06/2021; De nos jours, il existe de nombreux sujets de recherches autour de la mobilité et de la transition numérique ayant besoin d'utiliser des applications sur smartphone. Le smartphone, doté d'une puce GNSS (Global Navigation Satellite System) et d'applications mobiles pouvant l'exploiter, peut par exemples servir d'outil principal à la réalisation d'enquêtes cherchant à étudier la mobilité de la population, que ça soit sur leur déplacements quotidiens ou leurs pratiques touristiques (Calvignac & Smolinski, 2017). Que l'on parle d'études sur la mobilité des voyageurs ou de recherches scientifiques en positionnement, nombreux sont les chercheurs à se demander quelle application ou quel logiciel utiliser. Il n'existe pas de méthodologie ou base de données permettant d'accompagner ce choix. Les études existantes se concentrent sur une zone géographique ou un usage unique comme le déplacement en transport en commun en Alsace (Dreyer, 2017). De plus, avec la révolution numérique de ces dernières années, le nombre d'applications mobiles autour de la mobilité ne cesse de croître. En janvier 2020, le Google Play Store, qui est une des banques d'applications pour les téléphones sous Android, contenait plus de 2.900.000 applications avec environ 45.000 nouvellement ajoutées au seul mois de janvier (AppTornado GmBH, 2020). 4,9% sont classées dans les catégories «Voyage et Local, Cartes et Navigation» (Clement, 2020), ce qui correspond à 122.500 applications sur ces thèmes, et ce, en 11 ans seulement d'existence du système d'exploitation Android. Il y a donc de quoi se perdre lors du choix d'une application. Une autre problématique souvent constatée est liée à la difficulté d'évaluer la qualité et l'intégrité des données recueillies par les applications mobiles (Desvignes & Jacquot, 2014). Si l'on prend pour exemple une trace GPS sur un fond de carte, fournie par une application mobile, il est souvent difficile d'estimer la précision réelle de celle-ci. En conséquence, les études des traces peuvent aboutir à une incompréhension sur les écarts relevés. Il est compliqué de tirer des conclusions sur des données que l'on ne maîtrise mal. Pour aider et faciliter le choix d'applications numériques, une enquête en ligne a été réalisée afin de recenser et de caractériser les applications et logiciels en lien avec la mobilité. Cette étude est au coeur de ce papier. Elle s'adressait au personnel de l'Université Gustave Eiffel et comprenait des questions sur les caractéristiques de chaque application. Le but de cette enquête est d'alimenter la construction d'une plateforme originale de recensement qualifié des applications et logiciels afin d'accompagner la recherche multicritères d'applications numériques sur la mobilité. Une innovation de cette plateforme est d'associer à chaque application un ou plusieurs correspondants disponibles et connaissant l'application pour répondre aux interrogations du futur utilisateur et ainsi bénéficier de leur expérience.

The growing development of smart objects offers new opportunities for locating the connected traveler. However, tracking the pedestrian's trajectory remains problematic and navigation applications do not propose to track the traveller's trajectory on a multimodal scale autonomously. This work focuses on the implementation of a single solution capable of locating 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 themovement direction is constrained using inertial signals. These elements were used in a second time and adapted to implement a new method of pedestrian localization with a handheld sensor. The PDR approach which is an inertial navigation technique using dead reckoning 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 direction prediction and to obtain a consistent and smoothed estimate. GNSS measurements are used to correct the velocity 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 experiments in real conditions are conducted to analyze the performance 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 modes de déplacement et quel que soit l'environnement, à partir de capteurs inertiels, magnétiques 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 d'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 traveler. However, tracking the pedestrian's trajectory remains problematic and navigation applications do not propose to track the traveller's trajectory on a multimodal scale autonomously. This work focuses on the implementation of a single solution capable of locating 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 themovement direction is constrained using inertial signals. These elements were used in a second time and adapted to implement a new method of pedestrian localization with a handheld sensor. The PDR approach which is an inertial navigation technique using dead reckoning 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 direction prediction and to obtain a consistent and smoothed estimate. GNSS measurements are used to correct the velocity 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 experiments in real conditions are conducted to analyze the performance 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 modes de déplacement et quel que soit l'environnement, à partir de capteurs inertiels, magnétiques 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 d'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.

2020 IEEE Intelligent Vehicles Symposium, LAS VEGAS, ETATS-UNIS, 20-/10/2020 - 23/10/2020; 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.

Groupede Travail GNSS et Positionnementde la CNIG, Bouguenais, FRANCE, 16-/10/2020 - 16/10/2020; Groupe de Travail GNSS & Positionnement du CNIG (conseil national de l'information géographique).

Zero velocity update is a common and efficient approach to bound the accumulated error growth for footmounted inertial navigation system. Thus a robust zero velocity detector (ZVD) for all kinds of locomotion is needed for high accuracy pedestrian navigation systems. In this paper, we investigate twomachine learning-basedZVDs: Histogrambased Gradient Boosting (HGB) and Random Forest (RF), aiming at adapting to different motion types while reducing the computation costs compared to the deep learningbased detectors. A complete data pre-processing procedure, including a feature engineering study and data augmentation techniques, is proposed. A motion classifier based on HGB is used to distinguish ?single support? and ?double float? motions. This concept is different from the traditional locomotion classification (walking, running, stair climbing) since it merges similar motions into the same class. The proposed ZVDs are evaluated with inertial data collected by two subjects over a 1.8 km indoor/outdoor pathwith differentmotions and speeds. The results showthatwithout huge training dataset, these two machine learning-based ZVDs achieve better performances (55 cm positioning accuracy) and lower computational costs than the two deep learning-based Long Short-TermMemory methods (1.21 m positioning accuracy).

This paper provides a multiple sensor dataset collected by the CyborgLOC team during the intermediate competition of the Challenge MALIN (MAitrise de la localisation INdoor), which is a competition for indoor/outdoor real-time positioning. The sensors, including a GNSS receiver Ublox NEO-M8N, a Realsense D435i stereo camera, three Xsens MTi-300 and one PERSY (PEdestrian Reference SYstem), are mounted on different parts of the subject's body. The PERSY is a foot-mounted positioning device with a tri-axial accelerometer, a tri-axial gyroscope, a tri-axial magnetometer as well as a GNSS receiver Ublox M8T. The two scenarios are designed in a training centre of firefighters CFIS (Fire and Rescue Training Centre) in Blois, France to simulate the situation of firefighters during interventions. With total distances around 2 km for each scenario, the travelled trajectories passed through challenging environments including indoor, outdoor, urban canyon. The indoor part contains different stair levels, from the underground up to the 6th floor. The travel modes are vehicles and pedestrians. Several classical activities of firefighters are realized such as walking, running, stair-climbing, side-walking, crawling, passing above/below obstacles, carrying a stretcher, ladder climbing, etc. High accurate ground truth of stationary points and enclosing volumes are provided by the organizers of the competition, i.e., the Directorate General of Armaments (DGA: Direction Generale de l'Armement). Provided with raw data, they allow the evaluation of the positioning performances. This dataset is available on the data repository https://doi.org/10.5281/zenodo.4290789 .

47ème Congrès ATEC ITS France, PARIS, FRANCE, 22-/01/2020 - 23/01/2020; Le domaine de la géolocalisation s'est doté d'une méthodologie de référence indispensable pour évaluer et caractériser des terminaux GNSS destinés à la navigation terrestre. La série de normes EN16803 a pour objectif d'éclairer les choix des utilisateurs et de simplifier l'intégration des technologies satellitaires aux systèmes embarqués. Elle s'appuie sur une technique de métrologie instrumentale, identifiée sous le terme « Record & Replay » et offrant plusieurs avantages-clefs : - Restituer des environnements d'essais « Reproductibles » et « Représentatifs » du monde réel ; - Dissocier les erreurs de « Justesse » et de « Fidélité » pour enrichir les analyses ; - Etablir des scénarios de référence fiables pour « comparer les résultats de mesures ».

4th GNSS Raw Measurements Taskforce Workshop, PRAGUE, TCHÈQUE, RÉPUBLIQUE, 27-/05/2020 - 28/05/2020; Quelle stratégie de mise en oeuvre adopter pour un positionnement précis sur les smartphones?

The objective of this feasibility study is to assess the feasibility and limitations of using a simple approach (peak detection algorithms) to detect steps and segment the walking motion into gait cycles or strides and thus compute gait variability as the standard deviation of stride time.

Le concours IPIN 2019, sixième d'une série de concours IPIN, s'est déroulé dans l'espace de recherche du CNR de Pise (IT), intégré au programme de la conférence IPIN 2019. Il comprenait deux pistes en temps réel sur site et trois pistes hors site. Les quatre pistes présentées dans ce document ont été placées dans le même environnement, constitué de deux bâtiments proches l'un de l'autre pour une surface totale utilisable de 1000 m2 à l'extérieur et de 6000 m2 à l'intérieur sur trois étages, avec une longueur totale de piste supérieure à 500 m. Les concours IPIN, basés sur le cadre EvAAL, ont eu pour but de comparer les performances de précision des systèmes de positionnement personnel dans des conditions équitables et réalistes : les éditions précédentes du concours ont eu lieu dans le cadre de grandes conférences, sur des campus universitaires et dans un centre commercial. La précision de positionnement est calculée alors que la personne portant le système testé marche à une vitesse normale, utilise un ascenseur et monte et descend des escaliers ou s'arrête brièvement à des points donnés. Cet article présente l'édition 2019 : les résultats des pistes sur site montrent les performances des systèmes de pointe dans des conditions réelles, tandis que ceux des pistes hors site permettent de comparer les algorithmes de positionnement dans le même environnementIPIN 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 m2 outdoors and and 6000 m2 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 go up and down stairs or briefly stops at given points. This paper presents the 2019 edition: results of the on-site Tracks show the performance of stateof- the-art systems in real-world settings, while those of off-site Tracks allow comparison of positioning algorithms in the same environment

UCP, 3rd edition of the User Consultation Platform, Virtuel, , 01-/12/2020 - 02/12/2020; Comité européen de normalisation des normes GNSS pour la route et le véhicule autonome. Avantages de l'utilisation de la norme EN16803 pour le secteur automobile. Présentation.

Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo-ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5 degrees in texting mode and 12 degrees in pocket mode.

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, FRANCE, 24-/09/2018 - 27/09/2018; To support pedestrian navigation in urban and indoor spaces, an accurate pose estimate (i.e. 3D position and 3D orientation) of an equipment held in hand constitutes an essential point in the development of mobility assistance tools (e.g. Augmented Reality applications). On the assumption that the pedestrian is only equipped with general public devices, the pose estimation is restricted to the use of low-cost sensors embedded in the latter (i.e, an Inertial and Magnetic Measurement Unit and a monocular camera). In addition, urban and indoor spaces, comprising closely-spaced buildings and ferromagnetic elements, constitute challenging areas for sensor pose estimation during large pedestrian displacements. However, the recent development and provision of 3D Geographical Information System content by cities constitutes a wealth of data usable for pose estimation. To address these challenges, we propose an autonomous sensor fusion framework for pedestrian hand-held device pose estimation in urban and indoor spaces. The proposed solution integrates inertial and magnetic-based attitude estimation, monocular Visual Odometry with pedestrian motion estimation for scale estimation and known 3D geospatial object recognition-based absolute pose estimation. Firstly, this allows to continuously estimate a qualified pose of the device held in hand. Secondly, an absolute pose estimate enables to update and to improve the positioning accuracy. To assess the proposed solution, experimental data has been collected, for four different people, on a 0.5 km pedestrian walk in an urban space with sparse known objects and indoors passages. According to the performance evaluation, the sensors fusion process enhanced the pedestrian localization in areas where conventional hand-held systems were not accurate or available.