构建首个室内室外无缝切换的激光点云定位基准数据集
RoboLoc: A Benchmark Dataset for Point Place Recognition and Localization in Indoor-Outdoor Integrated Environments
- 采集真实机器人轨迹，涵盖室内外连续过渡场景
- 包含多类地形与楼层变化，支持跨域定位性能评估
- 适配自动驾驶与机器人导航系统研发测试
鲁棒的场景识别对机器人在复杂环境中实现可靠定位至关重要，尤其在频繁发生室内外转换的场景中。然而，现有基于激光雷达的数据集多聚焦于户外环境，缺乏真实的域间转换。本文提出 RoboLoc，一个面向无 GPS 室内外环境点云定位的基准数据集。该数据集包含真实机器人轨迹、多样化的高程变化以及从结构化室内到非结构化室外的自然过渡。我们对多种前沿模型（点、体素、BEV 架构）进行了基准测试，揭示其在域转移下的泛化能力差异。RoboLoc 为机器人与自主导航系统开发多域定位算法提供了真实可靠的测试平台。
原文摘要 · Abstract (English)
Robust place recognition is essential for reliable localization in robotics, particularly in complex environments with frequent 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
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