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Robust and accurate localization is an essential component for roboticnavigation and autonomous driving. The use of cameras for localization withhigh definition map (HD Map) provides an affordable localization sensor set.Existing methods suffer from pose estimation failure due to error prone dataassociation or initialization with accurate initial pose requirement. In thispaper, we propose a cost-effective vehicle localization system with HD map forautonomous driving that uses cameras as primary sensors. To this end, weformulate vision-based localization as a data association problem that mapsvisual semantics to landmarks in HD map. Specifically, system initialization isfinished in a coarse to fine manner by combining coarse GPS (Global PositioningSystem) measurement and fine pose searching. In tracking stage, vehicle pose isrefined by implicitly aligning the semantic segmentation result between imageand landmarks in HD maps with photometric consistency. Finally, vehicle pose iscomputed by pose graph optimization in a sliding window fashion. We evaluateour method on two datasets and demonstrate that the proposed approach yieldspromising localization results in different driving scenarios. Additionally,our approach is suitable for both monocular camera and multi-cameras thatprovides flexibility and improves robustness for the localization system.

Coarse-to-fine Semantic Localization with HD Map for Autonomous Driving in Structural Scenes