Real-Time Vehicle Global Localisation with a Single Camera in Dense Urban Areas: Exploitation of Coarse 3D City Models

Abstract

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.