Towards SLAM-Based Outdoor Localization using Poor GPS and 2.5D Building Models

In this paper, we address the topic of outdoor localization and tracking using monocular camera setups with poor GPS priors. We leverage 2.5D building maps, which are freely available from open-source databases such as OpenStreetMap. The main contributions of our work are a fast initialization method and a non-linear optimization scheme. The initialization upgrades a visual SLAM reconstruction with an absolute scale. The non-linear optimization uses the 2.5D building model footprint, which further improves the tracking accuracy and the scale estimation. A pose optimization step relates the vision-based camera pose estimation from SLAM to the position information received through GPS, in order to fix the common problem of drift. We evaluate our approach on a set of challenging scenarios. The experimental results show that our approach achieves improved accuracy and robustness with an advantage in run-time over previous setups.