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 on bundle adjustment with simple knowledge, i.e. the position and orientation of the camera with regard to the road and a coarse 3D model of the environment, as those provided by GIS database. First, we show that, thanks to specific SLAM-based constraints, the road homography can be expressed only with respect to the scale factor parameter. This allows the scale factor to be robustly and frequently estimated. Then, we propose to use the global information brought by 3D city models in order to correct the monocular SLAM error accumulation. Even with coarse 3D models, turnings give enough geometrical constraints to allow fitting the reconstructed 3D point cloud with the 3D model. Experiments on large-scale sequences (several kilometres) show that the entire process permits the real-time localisation of a car in city centre, even in real traffic condition.

This paper addresses the challenging issue of vision-based localization 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.

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 reduce those errors a posteriori. Our solution is based on a postprocessing algorithm that exploits additional geometric constraints, relative to the environment, to correct both the reconstructed geometry and the camera trajectory. These geometric constraints are obtained through a coarse 3D modelisation of the environment, similar to those provided by GIS database. First, we propose an original articulated transformation model in order to roughly align the SLAM reconstruction with this 3D model through a non-rigid ICP step. Then, to refine the reconstruction, we introduce a new bundle adjustment cost function that includes, in a single term, the usual 3D point/ID observation consistency constraint as well as the geometric constraints provided by the 3D model. Results on large-scale synthetic and real sequences show that our method successfully improves SLAM reconstructions. Besides, experiments prove that the resulting reconstruction is accurate enough to be directly used for global relocalization applications.