Guillaume Bresson

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This paper presents a real time monocular EKF SLAM process that uses only Cartesian defined landmarks. This representation is easy to handle, light and consequently fast. However, it is prone to linearization errors which can cause the filter to diverge. Here, we will first clearly identify and explain when those problems take place. Then, a solution, able(More)
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)
The localization of a vehicle in an unknown environment is often solved using simultaneous localization and mapping (SLAM) techniques. Many methods have been developed, each requiring a different amount of landmarks (map size), and thus of memory, to work efficiently. Similarly, the required computational time is quite variable from one approach to another.(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)
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 topological 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)