VectorReLoc: Reliable Vectorized SD Map Visual Re-localization with Contrastive Feature Alignment

Standard-definition (SD) maps are widely used in autonomous driv-ing, but accurate ego-centric SD map retrieval typically relies on costly RTK-GNSS joint localization. In contrast, low-cost GNSS introduces meter-level 3-DoF pose offsets with high variance, yielding misaligned local maps that candegrade downstream planning and control. Prior visual re-localization methodsmostly rely on dense rasterized BEV representations, which are computation-heavy and easily distracted by scene content irrelevant to road geometry. We pro-pose VectorReLoc, the first sparse re-localization framework that directly alignsvectorized SD maps with online-constructed vectorized visual maps to estimatethe 3-DoF pose offset. VectorReLoc further introduces a feature alignment objec-tive that structures the embedding space for accurate offset regression, togetherwith a reliability predictor to identify potentially unreliable corrections and mit-igate silent failures. To enable realistic training and evaluation, we additionallypresent a large-scale dataset with paired RTK-retrieved and GNSS-retrieved SDmaps, providing real pose-offset labels rather than simulated noise used in priorwork. Experiments on public benchmarks and the proposed dataset demonstrateimproved accuracy, robustness, and efficiency over previous approaches.