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.