首个真实自更新高精地图数据集，解决模拟与现实差距问题。
ArgoTweak: Towards Self-Updating HD Maps through Structured Priors
- 通过原子级地图元素变化建模，实现可解释的自更新。
- 实测表明相比合成先验显著缩小仿真到现实的差距。
- 适合自动驾驶地图构建与持续学习研究者使用。
可靠的先验信息融合对自验证、自更新的高精地图至关重要。然而，现有公开数据集缺乏包含先验地图、当前地图和传感器数据的三元组，导致现有方法只能依赖合成先验，产生不一致并带来显著的sim2real差距。为此，我们提出ArgoTweak，首个包含真实地图先验的三元组数据集。其核心采用双射映射框架，将大规模修改分解为细粒度的原子级地图元素变化，保证可解释性。该范式使变化检测与融合更精准，同时以高保真度保留未变化元素。实验表明，基于ArgoTweak训练模型能显著降低sim2real差距。大量消融实验进一步凸显结构化先验与详细变化标注的重要性。ArgoTweak建立了可解释、先验辅助高精地图的基准，推动可扩展的自进化地图解决方案。数据集、基线、地图修改工具箱及更多资源详见 https://kth-rpl.github.io/ArgoTweak/。
原文摘要 · Abstract (English)
Reliable integration of prior information is crucial for self-verifying and self-updating HD maps. However, no public dataset includes the required triplet of prior maps, current maps, and sensor data. As a result, existing methods must rely on synthetic priors, which create inconsistencies and lead to a significant sim2real gap. To address this, we introduce ArgoTweak, the first dataset to complete the triplet with realistic map priors. At its core, ArgoTweak employs a bijective mapping framework, breaking down large-scale modifications into fine-grained atomic changes at the map element level, thus ensuring interpretability. This paradigm shift enables accurate change detection and integration while preserving unchanged elements with high fidelity. Experiments show that training models on ArgoTweak significantly reduces the sim2real gap compared to synthetic priors. Extensive ablations further highlight the impact of structured priors and detailed change annotations. By establishing a benchmark for explainable, prior-aided HD mapping, ArgoTweak advances scalable, self-improving mapping solutions. The dataset, baselines, map modification toolbox, and further resources are available at https://kth-rpl.github.io/ArgoTweak/.
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