MapAware SLAM with Sparse Map Features Localization is a key capability for autonomous vehicles. HighDefinition maps are a popular method to represent the environment and to enable precise localization. However, the creation is very demanding and it is not always guaranteed to receive accurate map information, especially for unstructured areas. In this paper, we introduce a novel probabilistic localization and mapping framework that brings together the advantages of sparse feature maps, multitarget tracking for landmark detection, probabilistic global vehicle localization and a graphbased formulation to achieve a consistent map. The frontend of our Simultaneous Localization and Mapping framework is based on Monte Carlo Localization. Our novel measurement model integrates a virtual topological PathMap with sparse map features to obtain global localization. The graphbased backend optimizes online the vehicle trajectory and the landmarksxe2x80x99 configuration to create a globally aligned map. Furthermore, our method allows weaker requirements in terms of accuracy of the sparse feature map as we represent the degree of uncertainty by means of probabilistic distribution. Additionally, the sparse feature map representation needs substantially less memory than other approaches, which is an advantage for autonomous vehicles. The framework has been tested and evaluated in real experiments for several autonomous runs. The results demonstrate the robustness of our system.

Exploiting Sparse Semantic HD Maps for SelfDriving Vehicle Localization In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry and LiDAR intensitybased localizers. This is important as selfdriving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensitybased approaches.