Development of GNSS/INS/SLAM Algorithms for Navigation in Constrained Environments

For land vehicles, the requirements of the navigation solution in terms of accuracy, integrity, continuity and availability are more and more stringent, especially with the development of autonomous vehicles. This type of application requires a navigation system not only capable of providing an accurate and reliable position, velocity and attitude solution continuously but also having a reasonable cost. In the last decades, GNSS has been the most widely used navigation system especially with the receivers decreasing cost over the years. However, despite of its capability to provide absolute navigation information with long time accuracy, this system suffers from problems related to signal propagation especially in urban environments where buildings, trees and other structures hinder the reception of GNSS signals and degrade their quality. This can result in significant positioning error exceeding in some cases a kilometer. Many techniques are proposed in the literature to mitigate these problems and improve the GNSS accuracy. Unfortunately, all these techniques have limitations. A possible way to overcome these problems is to fuse “good” GNSS measurements with other sensors having complementary characteristics. In fact, by exploiting the complementarity of sensors, hybridization algorithms can improve the navigation solution compared to solutions provided by each stand-alone sensor. Generally, the most widely implemented hybridization algorithms for land vehicles fuse GNSS measurements with inertial and/or odometric data. Thereby, these Dead-Reckoning (DR) sensors ensure the system continuity when GNSS information is unavailable and improve the system performance when GNSS signals are degraded, and, in return the GNSS limits the drift of the DR solution if it is available. However the performance achieved by this hybridization depends thoroughly on the quality of the DR sensor used especially when GNSS signals are degraded or unavailable. Therefore, this Ph.D. thesis, which is part of a common French research project involving two laboratories and three companies, aims at extending the classical hybridization architecture by including other sensors capable of improving the navigation performances while having a low cost and being easily embeddable. For this reason, the use of vision-based navigation techniques to provide additional information is proposed in this thesis. In fact, cameras have become an attractive positioning sensor recently with the development of Visual Odometry and Simultaneous Localization and Mapping (SLAM) techniques, capable of providing accurate navigation solution while having reasonable cost. In addition, visual navigation solutions have a good quality in textured environments where GNSS is likely to encounter bad performance. Therefore, this work focuses on developing a multi-sensor fusion architecture integrating visual information with the previously mentioned sensors. In particular, the contribution of this information to improve the vision-free navigation system performance is highlighted. The proposed architecture respects the project constraints consisting of developing a versatile and modular low-cost system capable of providing continuously a good navigation solution, where each sensor may be easily discarded when its information should not be used in the navigation solutio

Review and classification of vision-based localisation techniques in unknown environments

International audienceThis study presents a review of the state-of-the-art and a novel classification of current vision-based localisation techniques in unknown environments. Indeed, because of progresses made in computer vision, it is now possible to consider vision-based systems as promising navigation means that can complement traditional navigation sensors like global navigation satellite systems (GNSSs) and inertial navigation systems. This study aims to review techniques employing a camera as a localisation sensor, provide a classification of techniques and introduce schemes that exploit the use of video information within a multi-sensor system. In fact, a general model is needed to better compare existing techniques in order to decide which approach is appropriate and which are the innovation axes. In addition, existing classifications only consider techniques based on vision as a standalone tool and do not consider video as a sensor among others. The focus is addressed to scenarios where no a priori knowledge of the environment is provided. In fact, these scenarios are the most challenging since the system has to cope with objects as they appear in the scene without any prior information about their expected position

Développement d’Algorithmes GNSS/INS/SLAM pour la Navigation en Milieux Contraints

For land vehicles, the requirements of the navigation solution in terms of accuracy, integrity, continuityand availability are more and more stringent, especially with the development of autonomous vehicles.This type of application requires a navigation system not only capable of providing an accurate andreliable position, velocity and attitude solution continuously but also having a reasonable cost. In thelast decades, GNSS has been the most widely used navigation system especially with the receiversdecreasing cost over the years. However, despite of its capability to provide absolute navigationinformation, this system suffers from problems related to signal propagation especially in urbanenvironments where buildings, trees and other structures hinder the reception of GNSS signals anddegrade their quality. A possible way to overcome these problems is to fuse good GNSS measurementswith other sensors having complementary characteristics. Generally, the most widely implementedhybridization algorithms for land vehicles fuse GNSS measurements with inertial and/or odometric data.However, the performance achieved by this hybridization depends thoroughly on the quality of theinertial/odometric sensor used especially when GNSS signals are degraded or unavailable. Therefore,this Ph.D. thesis, aims at extending the classical hybridization architecture by including other sensorscapable of improving the navigation performances while having a low cost and being easily embeddable.For this reason, the use of vision-based navigation techniques to provide additional information isproposed in this thesis. In particular, the SLAM technique is investigated. Therefore, this work focuseson developing a multi-sensor fusion architecture integrating visual information with the previouslymentioned sensors. In particular, the study of the contribution of this information to improve the visionfreenavigation system performance is perfomrmed.Les exigences en termes de précision, intégrité, continuité et disponibilité de la navigation terrestre,consistant à estimer la position, la vitesse et l’attitude d’un véhicule, sont de plus en plus strictes, surtoutdepuis le développement des véhicules autonomes. Ce type d’applications nécessite un système denavigation non seulement capable de fournir une solution de navigation précise et fiable, mais aussiayant un coût raisonnable. Durant les dernières décennies, les systèmes de navigation par satellites(GNSS) ont été les plus utilisés pour la navigation, surtout avec la baisse continue des coûts desrécepteurs. Cependant, malgré sa capacité à fournir des informations de navigation absolue avec unebonne précision dans des milieux dégagés, l’utilisation du GNSS dans des milieux contraints est limitéeà cause des problèmes liés à la propagation des signaux. Ce problème peut être surmonté en fusionnantles bonnes mesures GNSS avec les mesures d'autres capteurs ayant des caractéristiquescomplémentaires. Les algorithmes d'hybridation les plus largement mis en oeuvre pour les véhiculesterrestres fusionnent les mesures GNSS avec des données inertielles et / ou odométriques. Cependant,les performances obtenues par cette hybridation dépendent énormément de la qualité du capteurinertiel/odométrique utilisé, surtout lorsque les signaux GNSS sont dégradés ou indisponibles. Parconséquent, cette thèse, vise à enrichir l’architecture d'hybridation en incluant d'autres mesures decapteurs capables d'améliorer les performances de navigation tout en disposant d'un système bas coût etfacilement embarquable. C’est pourquoi l'utilisation de la technique de navigation SLAM basées sur lavision pour fournir des informations supplémentaires est proposée dans cette thèse. Par conséquent, cetravail se concentre sur le développement d'une architecture de fusion multi-capteurs fusionnantl’information visuelle fournie par le SLAM avec les capteurs précédemment mentionnés et étudie enparticulier la contribution de l'utilisation de cette information pour améliorer les performances dusystème de navigatio

A Low-cost GNSS/IMU/Visual monoSLAM/WSS Integration Based on Federated Kalman Filtering for Navigation in Urban Environments

International audienceCar navigation performance improvement is a subject of great interest nowadays especially with the development of autonomous car navigation. In urban environments, it is often difficult to rely on standalone Global Navigation Satellite System (GNSS) to obtain continuously an accurate and reliable navigation solution. In fact, the presence of buildings and other structures hindering the reception of GNSS signals (blockage, multipath, NLOS, poor geometry, etc.) makes it difficult for GNSS to provide accurate, continuous and reliable navigation solution in such an environment. A possible solution for this problem is to fuse information from a limited number of GNSS measurements and other sensors in order to enhance the system performance in terms of accuracy and availability. In this paper, we propose an integrated navigation system that fuses different sensor information in order to improve the car navigation performance in urban environments. A Low-cost navigation solution is proposed since the intended application is cost-sensitive. The proposed solution integrates information from an Inertial Measurement Unit (IMU), a GNSS receiver, a Wheel Speed Sensor (WSS) and a vision module based on monocular Simultaneous Localization And Mapping (SLAM). Motion constraints related to the movement of a land vehicle on the ground are also taken into account