针对无人机激光定位精度低的问题，提出SOAR框架提升复杂飞行下的定位性能。
SOAR: Regression-based LiDAR Relocalization for UAVs
- 设计局部保持注意力模块，增强视角变化下的几何特征鲁棒性
- 引入坐标无关初始化，降低对全局变换的敏感度，提升定位成功率40%
- 构建包含4个场景13条路径的大规模真实无人机激光数据集
基于回归的激光雷达重定位近年来成为高精度定位的有前景方案，但现有方法主要面向自动驾驶，在无人机因任意姿态变化和不规则飞行路径导致性能显著下降。本文提出SOAR，一种专为无人机设计的回归式激光雷达重定位框架。通过引入保留局部结构的滑动窗口注意力模块与局部不变的位置编码，有效捕捉对抗视角变化的判别性几何结构；同时设计坐标无关特征初始化模块，消除对全局变换的敏感性。此外，由于缺乏同步激光扫描、精确6-DoF位姿或多次遍历，现有无人机数据集难以真实评估激光重定位。为此，本文构建了大规模无人机激光定位数据集，涵盖4个场景、13条具有旋转和高度变化的飞行路径，提供更真实的基准。大量实验表明，本方法在UAVLoc上实现最先进性能，定位成功率提升40%，10米以上误差显著降低。
原文摘要 · Abstract (English)
Regression-based LiDAR relocalization has recently emerged as a promising solution for high-precision positioning in GNSS-denied environments. However, these methods are primarily tailored to autonomous driving, exhibiting significantly degraded accuracy in unmanned aerial vehicle (UAV) scenarios due to arbitrary pose variations and irregular flight paths. In this paper, we propose SOAR, a regression-based LiDAR relocalization framework for UAVs. Specifically, we introduce a locality-preserving sliding window attention module with locally invariant positional encoding to capture discriminative geometric structures robust to viewpoint changes. A coordinate-independent feature initialization module is further designed to eliminate sensitivity to global transformations. Furthermore, most existing UAV datasets are limited to evaluate LiDAR relocalization in real-world, due to the lack of synchronized LiDAR scans, accurate 6-DoF poses, or multiple traversals. Thus, we construct a large-scale UAV LiDAR localization dataset with 4 scenes and 13 irregular paths exhibiting rotation and altitude variations, providing a more realistic benchmark for UAVs. Extensive experiments demonstrate that our method achieves state-of-the-art performance, improving the localization success rate by 40% and reducing mean error over 10m on UAVLoc. Our code and dataset will be released soon.
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