用特征混合与对比学习，实现无需地图的快速激光定位。
FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training
- 用冻结的通用骨干提取局部点特征，再通过MLP混合器融合预测位姿。
- 训练速度提升数量级，且在多个基准上定位精度优于现有方法。
- 适合需要快速适配新环境的自动驾驶、机器人导航场景。
无地图激光雷达定位系统通过直接从原始点云预测传感器位置与姿态，在不依赖大地图和描述符的情况下实现精准定位。然而，其训练时间长，难以快速适应新环境。为此，我们提出FlashMix，采用冻结的、场景无关的骨干网络提取局部点特征，并通过MLP混合器进行聚合以预测传感器位姿。利用局部特征缓存加速训练数个数量级，并结合度量学习或对比损失对聚合特征进行正则化，提升性能与收敛性。我们在多个激光雷达定位基准上评估了FlashMix，测试了不同正则化策略与聚合器，证明其在真实场景中实现快速且高精度定位的有效性。代码已开源：https://github.com/raktimgg/FlashMix。
原文摘要 · Abstract (English)
Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hinder rapid adaptation to new environments. To address this, we propose FlashMix, which uses a frozen, scene-agnostic backbone to extract local point descriptors, aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude, combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks, examining different regularizations and aggregators, demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://github.com/raktimgg/FlashMix.
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