CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving

• URL: http://arxiv.org/abs/2512.03510v1
• Date: Wed, 03 Dec 2025 07:06:18 GMT
• Title: CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving
• Authors: Zhijian Qiao, Zehuan Yu, Tong Li, Chih-Chung Chou, Wenchao Ding, Shaojie Shen,
• Abstract summary: CSMapping produces accurate semantic maps and topological road centerlines.<n>Experiments on nuScenes, Argoverse 2, and a large proprietary dataset achieve state-of-the-art semantic and topological mapping performance.
• Score: 23.921417146230738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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