Towards SLAMBased 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 opensource databases such as OpenStreetMap. The main contributions of our work are a fast initialization method and a nonlinear optimization scheme. The initialization upgrades a visual SLAM reconstruction with an absolute scale. The nonlinear optimization uses the 2.5D building model footprint, which further improves the tracking accuracy and the scale estimation. A pose optimization step relates the visionbased 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 runtime over previous setups.

VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multisession maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheelodometry in a state estimation framework further guarantees smooth and accurate pose estimates. In an extensive experimental evaluation on several hundreds of driving kilometers in challenging urban outdoor environments, we analyze the recall and accuracy of our localization system, investigate its key parameters and boundary conditions, and compare different types of feature descriptors. Our results show that VIZARD is able to achieve nearly 100% recall with a localization accuracy below 0.5m under varying outdoor appearance conditions, including at nighttime.