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Agrégation d'information pour la localisation d'un robot mobile sur une carte imparfaite

Abstract : Most large modern cities in the world nowadays suffer from pollution and traffic jams. A possible solution to this problem could be to regulate personnal car access into center downtown, and possibly replace public transportations by pollution-free autonomous vehicles, that could dynamically change their planned trajectory to transport people in a fully on-demand scenario. These vehicles could be used also to transport employees in a large industrial facility or in a regulated access critical infrastructure area. In order to perform such a task, a vehicle should be able to localize itself in its area of operation. Most current popular localization methods in such an environment are based on so-called "Simultaneous Localization and Maping" (SLAM) methods. They are able to dynamically construct a map of the environment, and to locate such a vehicle inside this map. Although these methods demonstrated their robustness, most of the implementations lack to use a map that would allow sharing over vehicles (map size, structure, etc...). On top of that, these methods frequently do not take into account already existing information such as an existing city map and rather construct it from scratch. In order to go beyond these limitations, we propose to use in the end semantic high-level maps, such as OpenStreetMap as a-priori map, and to allow the vehicle to localize based on such a map. They can contain the location of roads, traffic signs and traffic lights, buildings etc... Such kind of maps almost always come with some degree of imprecision (mostly in position), they also can be wrong, lacking existing but undescribed elements (landmarks), or containing in their data elements that do not exist anymore. In order to manage such imperfections in the collected data, and to allow a vehicle to localize based on such data, we propose a new strategy. Firstly, to manage the classical problem of data incest in data fusion in the presence of strong correlations, together with the map scalability problem, we propose to manage the whole map using a Split Covariance Intersection filter. We also propose to remove possibly absent landmarks still present in map data by estimating their probability of being there based on vehicle sensor detections, and to remove those with a low score. Finally, we propose to periodically scan sensor data to detect possible new landmarks that the map does not include yet, and proceed to their integration into map data. Experiments show the feasibility of such a concept of dynamic high level map that could be updated on-the-fly.
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  • HAL Id : tel-01982085, version 1

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Laurent Delobel. Agrégation d'information pour la localisation d'un robot mobile sur une carte imparfaite. Robotique [cs.RO]. Université Clermont Auvergne, 2018. Français. ⟨NNT : 2018CLFAC012⟩. ⟨tel-01982085⟩

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