解决不同激光雷达类型间的定位匹配难题，提升跨设备识别能力。
HeLiOS: Heterogeneous LiDAR Place Recognition via Overlap-based Learning and Local Spherical Transformer
- 基于重叠区域学习与局部球面变换器，构建鲁棒全局描述符。
- 在异构激光雷达数据上实现高精度匹配，长时序识别效果优异。
- 适合需要跨设备兼容的机器人定位系统开发者使用。
LiDAR 地点识别是定位中的关键模块，用于将当前位置与先前观测环境进行匹配。现有方法主要针对旋转式激光雷达，利用其大视场角进行匹配，但随着多种新型激光雷达出现，跨类型数据匹配的重要性日益凸显，该问题长期被忽视。为此，我们提出 HeLiOS，一种专为异构激光雷达地点识别设计的深度网络，采用小局部窗口结合球面变换器，并通过基于最优传输的聚类分配生成鲁棒全局描述符。基于重叠区域的数据挖掘与引导三元组损失克服了传统距离挖掘和离散类别约束的局限性。HeLiOS 在公开数据集上验证，展示了在异构激光雷达场景下的优异表现，并包含长期识别评估，证明其可处理未见过的激光雷达类型。代码已开源，供机器人社区使用。
原文摘要 · Abstract (English)
LiDAR place recognition is a crucial module in localization that matches the current location with previously observed environments. Most existing approaches in LiDAR place recognition dominantly focus on the spinning type LiDAR to exploit its large FOV for matching. However, with the recent emergence of various LiDAR types, the importance of matching data across different LiDAR types has grown significantly-a challenge that has been largely overlooked for many years. To address these challenges, we introduce HeLiOS, a deep network tailored for heterogeneous LiDAR place recognition, which utilizes small local windows with spherical transformers and optimal transport-based cluster assignment for robust global descriptors. Our overlap-based data mining and guided-triplet loss overcome the limitations of traditional distance-based mining and discrete class constraints. HeLiOS is validated on public datasets, demonstrating performance in heterogeneous LiDAR place recognition while including an evaluation for long-term recognition, showcasing its ability to handle unseen LiDAR types. We release the HeLiOS code as an open source for the robotics community at https://github.com/minwoo0611/HeLiOS.
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