用开放地图提升激光雷达定位精度，实时且高效。
OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
- 通过可见性掩码对齐激光与地图数据，动态融合径向特征。
- 在KITTI数据集上召回率提升15.98%，推理速度加快12倍。
- 适合需要轻量级、实时定位的自动驾驶系统使用。
激光雷达场景识别是大规模户外环境中自主导航与跨模态定位的关键能力。现有方法多依赖预先构建的3D稠密地图或航拍影像，带来巨大存储开销且难以实时更新。本文提出OPAL框架，利用OpenStreetMap（OSM）作为轻量且实时更新的先验信息。核心创新在于通过两个精心设计模块弥合稀疏激光扫描与结构化OSM数据之间的域差异：一是跨模态可见性掩码，用于识别两模态中可观察区域以指导特征对齐；二是自适应径向融合模块，动态整合径向特征生成判别性全局描述符。在KITTI和KITTI-360数据集上的大量实验表明，OPAL在1米阈值下顶1匹配的召回率提升15.98%，相比最先进方法推理速度提升12倍。代码与数据已公开于https://github.com/kang-1-2-3/OPAL。
原文摘要 · Abstract (English)
LiDAR place recognition is a critical capability for autonomous navigation and cross-modal localization in large-scale outdoor environments. Existing approaches predominantly depend on pre-built 3D dense maps or aerial imagery, which impose significant storage overhead and lack real-time adaptability. In this paper, we propose OPAL, a novel framework for LiDAR place recognition that leverages OpenStreetMap (OSM) as a lightweight and up-to-date prior. Our key innovation lies in bridging the domain disparity between sparse LiDAR scans and structured OSM data through two carefully designed components. First, a cross-modal visibility mask that identifies observable regions from both modalities to guide feature alignment. Second, an adaptive radial fusion module that dynamically consolidates radial features into discriminative global descriptors. Extensive experiments on KITTI and KITTI-360 datasets demonstrate OPAL's superiority, achieving 15.98% higher recall at 1m threshold for top-1 retrieved matches, along with 12x faster inference speed compared to the state-of-the-art approach. Code and data are publicly available at: https://github.com/kang-1-2-3/OPAL.
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