GeoPR: Label-free LiDAR Place Recognition with Hybrid Geometric Graphs

LiDAR place recognition plays a crucial role in SLAM systems and robot localization. To address the drawback of existing hand-crafted descriptors—namely, their limited topological representation capability in complex scenes—and the high training cost and strong dependence of semantic-assisted methods, our study proposes a label-free hybrid geometric graph-based LiDAR place recognition algorithm, termed Geometric Place Recognition (GeoPR). GeoPR exploits the intrinsic geometric structural information of point clouds. Our algorithm adopts a preprocessing module to directly extract stable geometric primitives from raw point clouds as geometric nodes. On this basis, we design a hybrid geometric graph descriptor by introducing logarithmic distance weighting and Gaussian soft binning to enhance robustness against discretization errors and dynamic disturbances. During the matching process, our research adopts a two-stage “global search - geometric verification” framework: using a KD-tree for rapid candidate screening, followed by a geometric consistency verification module based on correspondences to eliminate false positive loop closures. Experiments on public datasets show that, without any training data or GPU acceleration, GeoPR achieves significantly higher precision and recall than mainstream hand-crafted methods, while its scene adaptability is comparable to advanced deep learning methods. Therefore, this work provides an efficient and reliable place-recognition solution for automatic driving and lightweight mobile robot platforms.