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In this system paper, we propose a real-time car localisation process in dense urban areas by using a single perspective camera and a priori on the environment. To tackle this problem, it is necessary to solve two well-known monocular SLAM limitations: scale factor drift and error accumulation. The proposed idea is to combine a monocular SLAM process based(More)
In the past few years, lots of works were achieved on Simultaneous Localization and Mapping (SLAM). It is now possible to follow in real time the trajectory of a moving camera in an unknown environment. However, current SLAM methods are still prone to drift errors, which prevent their use in large-scale applications. In this paper, we propose a solution to(More)
This paper addresses the challenging issue of vision-based localiza-tion in urban context. It briefly describes our contributions in large environments modeling and accurate camera localization. The efficiency of the resulting system is illustrated through Augmented Reality results on large trajectory of several hundred meters.
Monocular SLAM reconstruction algorithm advancements enable their integration in various applications: tra-jectometry, 3D model reconstruction, etc. However proposed methods still have drift limitations when applied to large-scale sequences. In this paper, we propose a post-processing algorithm which exploits a CAD model to correct SLAM reconstructions. The(More)
Nous proposons un algorithme qui corrige a posteriori les dérives des méthodes de SLAM. Celui-ci exploite la connaissance a priori d'un modèle 3D simple de l'envi-ronnement. Notre méthode se déroule en deux étapes suc-cessives. Tout d'abord, un alignement grossier de la reconstruction SLAM avec le modèle 3D est calculé. Pour cela, nous proposons un(More)
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