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Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors / Paul Chauchat (2020)  

Public Titre : Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors Type de document : Thèse/HDR Auteurs : Paul Chauchat, Auteur ; Silvère Bonnabel, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2020 Importance : 135 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université Paris Sciences et Lettres, Informatique temps réel, robotique, automatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] cadre conceptuel [Termes IGN] cartographie et localisation simultanées [Termes IGN] centrale inertielle [Termes IGN] filtre de Kalman [Termes IGN] itération [Termes IGN] lissage de données [Termes IGN] navigation inertielle [Termes IGN] robotique Index. décimale : THESE Thèses et HDR Résumé : (auteur) Mobile systems need to locate themselves ever more accurately, and in ever more complex situations. This is in particular true for autonomous systems, for which controlling the position error is a critical safety issue. To this end, they are endowed with various sensors, the data of which are fused to obtain an estimate of the vehicle’s location, either globally (with the GPS for instance), or locally, with respect to its surroundings (with cameras for instance). This thesis investigates algorithms for localisation by sensor fusion, namely filtering and especially smoothing, when the mobile is equipped with high-grade inertial sensors. The first part deals with the nonlinear consequences of the use of high-grade inertial sensors, and demonstrates how the nonlinear structure of both filtering and smoothing algorithms may be improved by leveraging the invariant filtering framework. The second part deals with the problems incurred by the linear solvers that are used at each step of nonlinear smoothing algorithms as a result of having highly precise sensors. It introduces a novel least-squares linear solver that solves the issues. Note de contenu : Introduction I- From Invariant filtering to invariant smoothing II- Navigation with highly precise sensors Conclusion Numéro de notice : 28576 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/POSITIONNEMENT Nature : Thèse française Note de thèse : thèse de Doctorat : Informatique temps réel, robotique, automatique : Paris Sciences et Lettres : 2020 Organisme de stage : Centre de robotique (Paris) En ligne : https://pastel.archives-ouvertes.fr/tel-02887295/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97843