SOAR: UAV向け回帰ベースLiDAR再位置推定
SOAR: Regression-based LiDAR Relocalization for UAVs
UAVの任意姿勢・不規則飛行経路に対応する回帰ベースLiDAR再位置推定フレームワークSOARを提案し、大規模UAV LiDARデータセットを構築して従来手法を大幅に上回る精度を実現した。
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著者: Hengyu Mu, Jianshi Wu, Yuxin Guo, XianLian Lin, Qingyong Hu, Sheng Ao, Chenglu Wen, Cheng Wang
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
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.
