用卫星图替代3D地图，低成本实现城市级激光定位
L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery
- 用卫星图像作为地图代理，跨视角对齐点云与遥感图像特征
- 在100平方公里内，90%以上定位误差小于30米，无需微调
- 适合大规模城市自动驾驶与无人导航场景
针对传统基于激光雷达的定位依赖昂贵且耗时的三维地图问题，本文构建了包含约11万张遥感子图和1.3万张激光点云子图的LiRSI-XA数据集，并提出L2RSI方法，利用高分辨率遥感影像实现跨视角激光雷达定位。该方法通过语义域中的特征对齐，解决跨视角、跨模态匹配难题；引入基于粒子估计的概率传播机制，有效融合时空信息以优化位置预测。实验表明，在100km²范围内，83.27%的点云子图可被准确识别在30m半径内（top-1），具备强泛化能力且无需微调。项目主页公开：https://shizw695.github.io/L2RSI/
原文摘要 · Abstract (English)
We tackle the challenge of LiDAR-based place recognition, which traditionally depends on costly and time-consuming prior 3D maps. To overcome this, we first construct LiRSI-XA dataset, which encompasses approximately $110,000$ remote sensing submaps and $13,000$ LiDAR point cloud submaps captured in urban scenes, and propose a novel method, L2RSI, for cross-view LiDAR place recognition using high-resolution Remote Sensing Imagery. This approach enables large-scale localization capabilities at a reduced cost by leveraging readily available overhead images as map proxies. L2RSI addresses the dual challenges of cross-view and cross-modal place recognition by learning feature alignment between point cloud submaps and remote sensing submaps in the semantic domain. Additionally, we introduce a novel probability propagation method based on particle estimation to refine position predictions, effectively leveraging temporal and spatial information. This approach enables large-scale retrieval and cross-scene generalization without fine-tuning. Extensive experiments on LiRSI-XA demonstrate that, within a $100km^2$ retrieval range, L2RSI accurately localizes $83.27\%$ of point cloud submaps within a $30m$ radius for top-$1$ retrieved location. Our project page is publicly available at https://shizw695.github.io/L2RSI/.
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