用扩散模型融合低精度导航图与实时感知数据，构建动态高精地图。
NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
- 基于扩散模型迭代去噪，融合传感器数据与旧地图先验
- 在nuScenes上100米范围提升21.4%，大范围效果更优
- 适合需要实时更新高精地图的自动驾驶系统
精确的环境表征对自动驾驶至关重要，为安全高效导航提供基础。传统高精（HD）地图需预先提供静态道路基础设施信息，但现实世界持续变化，因此必须基于车载传感器数据在线构建。导航级标准分辨率（SD）地图虽广泛可用，但分辨率不足，无法直接部署。它们可作为粗略先验，引导在线地图构建过程。本文提出NavMapFusion，一种基于扩散模型的框架，通过高保真传感器数据和低保真导航地图进行迭代去噪。研究核心问题：(1) 低精度、可能过时的导航地图如何指导在线建图？(2) 扩散模型在地图融合中有哪些优势？结果表明，扩散模型为地图融合提供了稳健框架。关键洞见是：先验地图与在线感知间的差异自然对应于扩散过程中的噪声；一致区域增强建图，过时路段被抑制。在nuScenes基准上，以OpenStreetMap提供的粗略道路线为先验，100米范围内相对提升21.4%，更大感知范围下提升更显著，同时保持实时能力。该方法通过融合低质量先验与高质量感知数据，生成准确且实时的环境表征，推动更安全可靠的自动驾驶发展。代码已开源。
原文摘要 · Abstract (English)
Accurate environmental representations are essential for autonomous driving, providing the foundation for safe and efficient navigation. Traditionally, high-definition (HD) maps are providing this representation of the static road infrastructure to the autonomous system a priori. However, because the real world is constantly changing, such maps must be constructed online from on-board sensor data. Navigation-grade standard-definition (SD) maps are widely available, but their resolution is insufficient for direct deployment. Instead, they can be used as coarse prior to guide the online map construction process. We propose NavMapFusion, a diffusion-based framework that performs iterative denoising conditioned on high-fidelity sensor data and on low-fidelity navigation maps. This paper strives to answer: (1) How can coarse, potentially outdated navigation maps guide online map construction? (2) What advantages do diffusion models offer for map fusion? We demonstrate that diffusion-based map construction provides a robust framework for map fusion. Our key insight is that discrepancies between the prior map and online perception naturally correspond to noise within the diffusion process; consistent regions reinforce the map construction, whereas outdated segments are suppressed. On the nuScenes benchmark, NavMapFusion conditioned on coarse road lines from OpenStreetMap data reaches a 21.4% relative improvement on 100 m, and even stronger improvements on larger perception ranges, while maintaining real-time capabilities. By fusing low-fidelity priors with high-fidelity sensor data, the proposed method generates accurate and up-to-date environment representations, guiding towards safer and more reliable autonomous driving. The code is available at https://github.com/tmonnin/navmapfusion
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