Étude de méthodes ensemblistes robustes pour une localisation multisensorielle intègre. Application à la navigation des véhicules en milieu urbain.

Abstract : In this thesis, confidence domains for vehicle localization are characterized by using robust interval methods. Positioning is of prime importance in mobile robotics and more specifically for intelligent vehicle applications. When position information is used in a safety-critical context, like autonomous vehicle navigation, an integrity method is needed to check that the positioning error stays within the limits specified for the mission. In aeronautical navigation, protection levels are defined as bounds on the position error associated to a given integrity risk. This work aims to compute a confidence domain in which the user in guaranteed to be located with a given integrity risk. The possible presence of outliers is handled by the use of robust set-membership methods. Sensor measurements and model parameters are prone to errors, which are often modeled by their probability distribution. In the set-membership working frame, errors can be represented by intervals, thus making the assumption of bounded errors. When guaranteed error bounds are unknown or too pessimistic, error bounds associated with a risk can be used. The risk taken on measurements is then propagated to the computed confidence domain. Global navigation satellite systems enable high precision absolute positioning in open sky environments, but measurements suffer from multipath and non-line-of-sight propagation in urban areas. Robustness to outliers is thus needed. To counter the lack of visible satellites in urban canyons, position is constrained by a 3D map of the drivable space and by using the proprioceptive sensors embedded in recent vehicles. This document presents three positioning methods based on a robust set inversion via interval analysis with GPS pseudorange measurements : * Snapshot computation of a position confidence domain, with GPS measurements and altitude constraint from a digital elevation model. * Use of a precise 3D model of the drivable space as a positioning constraint, and observation of the GPS receiver's clock drift. * Robust pose estimation from a sliding horizon of positions and proprioceptive measurements, constrained by a 3D map. These positioning methods have been implemented in real-time and tested with real data in difficult environments for satellite positioning.
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Contributor : Vincent Drevelle <>
Submitted on : Tuesday, July 3, 2012 - 2:05:45 PM
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Vincent Drevelle. Étude de méthodes ensemblistes robustes pour une localisation multisensorielle intègre. Application à la navigation des véhicules en milieu urbain.. Automatique. Université de Technologie de Compiègne, 2011. Français. ⟨tel-00679502v2⟩



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