Existing LGL methods typically consider only partial information (e.g., geometric features) from LiDAR observations or are designed for homogeneous LiDAR sensors, overlooking the uniformity in LGL. In this work, a uniform LGL method is proposed, termed UniLGL, which simultaneously achieves spatial and material uniformity, as well as sensor-type uniformity. The key idea of the proposed method is to encode the complete point cloud, which contains both geometric and material information, into a pair of BEV images (i.e., a spatial BEV image and an intensity BEV image). An end-to-end multi-BEV fusion network is designed to extract uniform features, equipping UniLGL with spatial and material uniformity. To ensure robust LGL across heterogeneous LiDAR sensors, a viewpoint invariance hypothesis is introduced, which replaces the conventional translation equivariance assumption commonly used in existing LPR networks and supervises UniLGL to achieve sensor-type uniformity in both global descriptors and local feature representations. Finally, based on the mapping between local features on the 2D BEV image and the point cloud, a robust global pose estimator is derived that determines the global minimum of the global pose on SE(3) without requiring additional registration. To validate the effectiveness of the proposed uniform LGL, extensive benchmarks are conducted in real-world environments, and the results show that the proposed UniLGL is demonstratively competitive compared to other State-of-the-Art LGL methods. Furthermore, UniLGL has been deployed on diverse platforms, including full-size trucks and agile Micro Aerial Vehicles (MAVs), to enable high-precision localization and mapping as well as multi-MAV collaborative exploration in port and forest environments, demonstrating the applicability of UniLGL in industrial and field scenarios.

翻译：现有LGL方法通常仅利用LiDAR观测中的部分信息（如几何特征），或针对同构LiDAR传感器设计，忽视了LGL的统一性。本文提出一种统一的LGL方法UniLGL，可同时实现空间与材质统一性以及传感器类型统一性。该方法的核心思想是将包含几何与材质信息的完整点云编码为一对BEV图像（即空间BEV图像与强度BEV图像）。通过设计端到端的多BEV融合网络提取统一特征，使UniLGL具备空间与材质统一性。为保障跨异构LiDAR传感器的鲁棒LGL，引入视点不变性假设，替代现有LPR网络常用的平移等变性假设，并监督UniLGL在全局描述子与局部特征表示中实现传感器类型统一性。最后，基于2D BEV图像局部特征与点云的映射关系，推导出鲁棒全局位姿估计器，可在无需额外配准的情况下确定SE(3)上的全局位姿最小值。为验证所提统一LGL的有效性，在真实环境中进行大量基准测试，结果表明UniLGL相比其他最新LGL方法具有显著竞争力。此外，UniLGL已部署于全尺寸卡车与敏捷微型飞行器等多样化平台，在港口与森林环境中实现高精度定位建图及多MAV协同探索，展示了其在工业与野外场景中的实用性。