RoboLoc: A Benchmark Dataset for Point Place Recognition and Localization in Indoor–Outdoor Integrated Environments

ABSTRACT 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.