Vehicle positioning in urban environments using particle filtering-based global positioning system, odometry, and map data fusion

This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter

Perception multi-capteurs pour la navigation par satellites en milieu urbain

Les travaux de thèse présentés dans ce mémoire portent sur l étude de la perception multi-capteurs pour la navigation par satellites. La problématique est d assurer un positionnement continu et efficace du véhicule en milieu urbain où les problèmes de masquages des satellites sont fréquents. Nous avons choisi d hybrider le capteur GPS avec des capteurs proprioceptifs à faible coût. Notre choix s est porté sur les odomètres qui sont disponibles en série sur des véhicules grand public. En cas de masquage GPS, les odomètres prennent alors le relais afin d assurer une localisation continue. On exploite également dans ce système d aide à la navigation des informations cartographiques qui apportent des connaissances topographiques permettant d accroître les performances en localisation. Ce problème de fusion d informations est résolu par des outils d estimation d état non-linéaire qui permettent une prise en compte optimale des différentes sources d informations, notamment à partir des méthodes de filtrage de Kalman unscented et particulaire. L originalité de ce travail repose sur une description de l odomètre que l on modélise comme un capteur de vitesse, ce qui permet de prendre en compte les erreurs inhérentes à ce type de capteur. La prise en compte des données cartographiques est faite de manière statistique à partir de la métrique de Mahalanobis. Nous proposons finalement une méthode de navigation en présence de masquages GPS partiels. Dans ce cas, le filtre fusionne les pseudo-distances disponibles même si leur nombre est insuffisant pour fournir une estimation de localisation GPS.This work deals with the multisensory perception for satellites navigation. The idea is to get a continuous and efficient positioning of the vehicule in urban environment where GPS outages occur. We hybrided a GPS receiver and low-cost dead reckoning sensors. One of the most popular is the odometer which is a sensor available as a standard component in Antilock Brakink System (ABS) of vehicules. When GPS fails, the odometers allow a continuous positioning. We also use a road map database which improves the vehicle positioning. To solve this multisensor fusion problem, we develop a solution based on an Unscented Kalman filter and a particle filter that allow a direct integration of the measurements. The originality of this work relies on a modeling of the odometer which is described like a speed sensor. The statistical use of the road map measurements is based on the Mahalanobis metric. Finally, we propose a navigation method when partial GPS outages occur. In this case, the filter also fuses the available pseudo-ranges even if they are not enough to get a GPS positioning.CALAIS-BU Sciences (621932101) / SudocSudocFranceF

A New Interval Based Algebraic Reconstruction Technique for Error Quantification of Emission Tomography Images

International audienceThis article presents a new algebraic method for reconstructing emission tomography images. This approach is mostly an interval extension of the conventional SIRT algorithm. One of the main characteristic of our approach is that the reconstructed activity associated with each pixel of the reconstructed image is an interval whose length can be considered as an estimate of the impact of the random variation of the measured activity on the reconstructed image. This work aims at investigating a new methodological concept for a reliable and robust quantification of reconstructed activities in scintigraphic images