PriorMaskMap：基于有偏先验的鲁棒在线矢量化地图构建
PriorMaskMap: Robust Online Vectorized Map Construction with Biased Priors
Haoming Xu · Wei Li · Yu Hu
中文摘要
在线矢量化地图构建为自动驾驶车辆提供实时语义与几何场景理解。利用先验地图可提升精度，但先验常因信息过时或临时道路变化而有偏。现有方法通常将先验视为完全可靠或仅考虑有限噪声模式，在先验不完美时性能严重下降。为此提出 PriorMaskMap，一个鲁棒的先验引导矢量化地图构建框架。其首个创新是 Prior-Aware Confidence Estimator（PACE），将输入栅格化地图与当前视觉观测对比，为可靠、错误、空白区域分配置信度分数，生成基础掩码以引导两个后续分支。一个分支中，Dual-Prior Query Generator（DPQG）选择性过滤并嵌入栅格化与矢量化先验，从可信地图结构初始化信息丰富的查询；另一分支中，Confidence-Guided Prior Fusion（CGPF）模块对掩码进行空间传播与细化，用细化后的掩码控制 BEV 特征与先验地图的融合，为地图解码器提供丰富上下文表示。在 nuScenes 数据集上的评估表明，PriorMaskMap 的建图精度优于现有方法，并在先验含明显错误时表现出显著鲁棒性。代码已发布于 https://github.com/healenrens/PriorMaskMap。
关键要点
- 01现有先验引导建图方法将先验视为可靠或仅考虑有限噪声，先验不完美时性能严重下降
- 02PACE 对比栅格化先验与视觉观测，为可靠、错误、空白区域分配置信度并生成掩码
- 03DPQG 过滤并嵌入栅格化与矢量化先验，从可信结构初始化查询；CGPF 细化掩码并控制 BEV 特征融合
- 04在 nuScenes 上建图精度优于现有方法，先验含明显错误时仍保持显著鲁棒性
解读
尚无解读。
原始英文摘要
arXiv:2610.39283v1 Announce Type: cross Abstract: Online vectorized map construction provides autonomous vehicles with essential, real-time semantic and geometric scene understanding. While leveraging prior maps presents an opportunity to improve accuracy, they are often biased due to outdated information or temporary road changes. Existing methods, which typically treat priors as fully reliable or only consider limited noise patterns, suffer severe performance degradation when these priors are imperfect. To address this, we propose PriorMaskMap, a robust framework for prior-guided vectorized map construction. Its first innovation is a Prior-Aware Confidence Estimator (PACE) that evaluates the input rasterized map against current visual observations. It assigns confidence scores to regions classified as reliable, erroneous, or blank, creating a foundational mask that guides two subsequent branches. In one branch, the Dual-Prior Query Generator (DPQG) selectively filters and embeds both rasterized and vectorized priors to initialize informative queries from trustworthy map structures. In the other, the Confidence-Guided Prior Fusion (CGPF) module spatially propagates and refines the mask, using the refined version to govern the fusion of BEV features with the prior map, thereby providing rich contextual representations for the map decoder. Evaluation on the nuScenes dataset shows that PriorMaskMap achieves superior mapping precision over existing approaches and exhibits marked robustness when provided with priors containing apparent errors. Our code is released at https://github.com/healenrens/PriorMaskMap.