Robust place recognition is essential for reliable localization in robotics, particularly in complex environments with fre- quent indoor-outdoor transitions. However, existing LiDAR-based datasets often focus on outdoor scenarios and lack seamless domain shifts. In this paper, we propose RoboLoc, a benchmark dataset designed for GPS-free place recognition in indoor-outdoor environments with floor transitions. RoboLoc features real-world robot trajectories, diverse elevation profiles, and transitions between structured indoor and unstructured outdoor domains. We benchmark a variety of state-of-the-art models, point-based, voxel-based, and BEV-based architectures, highlighting their generalizability domain shifts. RoboLoc provides a realistic testbed for developing multi-domain localization systems in robotics and autonomous navigation

翻译：鲁棒的地点识别对于机器人实现可靠定位至关重要，尤其是在频繁发生室内外转换的复杂环境中。然而，现有的基于激光雷达的数据集通常侧重于室外场景，缺乏无缝的领域转换。本文提出了RoboLoc，一个专为无GPS室内外环境（包含楼层转换）中地点识别而设计的基准数据集。RoboLoc包含真实世界的机器人轨迹、多样化的高程剖面，以及结构化室内与非结构化室外领域之间的转换。我们对多种最先进的模型进行了基准测试，包括基于点、基于体素和基于鸟瞰图（BEV）的架构，重点评估了它们在领域转换中的泛化能力。RoboLoc为开发机器人及自主导航系统中的多领域定位系统提供了一个真实的测试平台。