Abstract
Crowdsourcing enables scalable autonomous driving map construction, but low-cost sensor noise hinders quality from improving with data volume. We propose CSMapping, a system that produces accurate semantic maps and topological road centerlines whose quality consistently increases with more crowdsourced data. For semantic mapping, we train a latent diffusion model on HD maps (optionally conditioned on SD maps) to learn a generative prior of real-world map structure, without requiring paired crowdsourced/HD-map supervision. This prior is incorporated via constrained MAP optimization in latent space, ensuring robustness to severe noise and plausible completion in unobserved areas. Initialization uses a robust vectorized mapping module followed by diffusion inversion; optimization employs efficient Gaussian-basis reparameterization, projected gradient descent zobracket multi-start, and latent-space factor-graph for global consistency. For topological mapping, we apply confidence-weighted k-medoids clustering and kinematic refinement to trajectories, yielding smooth, human-like centerlines robust to trajectory variation. Experiments on nuScenes, Argoverse 2, and a large proprietary dataset achieve state-of-the-art semantic and topological mapping performance, with thorough ablation and scalability studies.
Abstract (translated)
群采（Crowdsourcing）能够实现自动驾驶地图的大规模构建，但低成本传感器噪声阻碍了随着数据量增加而提高的质量。为此，我们提出了CSMapping系统，该系统可以生成准确的语义地图和拓扑道路中心线，其质量会随着更多众包数据的加入而持续提升。 在语义地图方面，我们在高精度（HD）地图（可选条件为标准定义（SD）地图）上训练了一个潜在扩散模型，以学习现实世界中地图结构的生成先验，无需配对的众包/高清地图监督。该先验通过约束最大后验概率优化在潜在空间中进行整合，确保了对于严重噪声具有鲁棒性，并且能在未观察到的区域实现合理的完成。 初始化阶段采用了一种稳健的矢量化映射模块并结合扩散逆向操作；优化则采用了高效的高斯基重参数化、投影梯度下降和多起点策略以及潜在空间因子图来保证全局一致性。 在拓扑地图方面，我们通过应用基于置信度加权k-medoids聚类和运动学精炼的轨迹方法，生成了光滑且具有人类特征的道路中心线，并具备应对轨迹变化的能力。 实验结果表明，在nuScenes、Argoverse 2以及一个大型专有数据集上进行的测试中，CSMapping在语义地图和拓扑地图性能方面均达到了最先进的水平。研究还包含了详细的消融分析及规模性研究以证明其有效性。
URL
https://arxiv.org/abs/2512.03510
