VCC: Vertical Feature and Circle Combined Descriptor for 3D Place Recognition

Loop closure detection remains a critical challenge in LiDAR-based SLAM, particularly for achieving robust place recognition in environments with rotational and translational variations. To extract more concise environmental representations from point clouds and improve extraction efficiency, this paper proposes a novel composite descriptor-the vertical feature and circle combined (VCC) descriptor, a novel 3D local descriptor designed for efficient and rotation-invariant place recognition. The VCC descriptor captures environmental structure by extracting vertical features from voxelized point clouds and encoding them into circular arc-based histograms, ensuring robustness to viewpoint changes. Under the same hardware, experiments conducted on different datasets demonstrate that the proposed algorithm significantly improves both feature representation efficiency and loop closure recognition performance when compared with the other descriptors, completing loop closure retrieval within 30 ms, which satisfies real-time operation requirements. The results confirm that VCC provides a compact, efficient, and rotation-invariant representation suitable for LiDAR-based SLAM systems.