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Premiered Jun 24, 2019
IROS 2019 (Accepted)
Localization is a key capability for autonomous vehicles. High-Definition 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 features maps, multi-target tracking for landmark detection, probabilistic global vehicle localization and a graph-based formulation to achieve a consistent map. The front-end of our Simultaneous Localization and Mapping (SLAM) framework is based on Monte Carlo Localization. Our novel measurement model integrates a virtual topological Path-Map with sparse map features to obtain global localization. The graph-based back-end optimizes online the vehicle trajectory and the landmarks’ 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 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.