Guillaume Bresson

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This paper presents a multi-vehicle decentralized SLAM algorithm. We expose the different problems involved by this decentralized setting, such as network aspects (data losses, latencies or bandwidth requirements) or data incest (double-counting information), and address them. In order to ease the data association process and also guarantee the consistency(More)
This paper presents a solution to the consistency problem of SLAM algorithms. We propose here to model the drift affecting the estimation process. The divergence is seen as a bias on the vehicle localization. By using such a model, we are able to guarantee the consistency of the localization. We developed a filter taking into account the divergence and(More)
—This paper presents a metric global localization in the urban environment only with a monocular camera and the Google Street View database. We fully leverage the abundant sources from the Street View and benefits from its topo-metric structure to build a coarse-to-fine positioning, namely a topolog-ical place recognition process and then a metric pose(More)
The localization of a vehicle is a central task of autonomous driving. Most of the time, it is solved by considering a single algorithm with a few sensors. In this paper, we propose a cooperative fusion architecture based on two main algorithms: a laser-based Simultaneous Localization And Mapping (SLAM) process and a lane detection and tracking approach(More)
With the fast development of Geographic Information Systems, visual global localization has gained a lot of attention due to the low price of a camera and the practical implications. In this paper, we leverage Google Street View and a monocular camera to develop a refined and continuous positioning in urban environments: namely a topological visual place(More)
Localization without prior knowledge can be a difficult task for a vehicle. An answer to this problematic lies in the Simultaneous Localization And Mapping (SLAM) approach where a map of the surroundings is built while simultaneously being used for localization purposes. However, SLAM algorithms tend to drift over time, making the localization inconsistent.(More)