DVMM: A Dual-View Combination Descriptor for Multi-Modal LiDARs Online Place Recognition

Abstract

Existing place recognition descriptors developed for single-agent SLAM struggle with multi-modal LiDAR differences in collaborative SLAM. To overcome this, we propose an online place recognition method for multi-modal LiDARs. This method introduces a dual-view combination descriptor, termed DVMM, by separately encoding azimuthal and vertical scene information. The place recognition process consists of two stages: loop closure detection and verification. In the detection stage, point clouds are projected onto an adaptive grid and a 1D azimuthal descriptor is generated via Gaussian-weighted column summation. The azimuthal descriptor is utilized to retrieve loop candidates through vector matching. In the verification stage, point clouds within a fixed height range are encoded as a binary occupancy image, which serves as the cross-section descriptor. Accurate loop closures are determined by performing image matching on the cross-section descriptors. We evaluate the proposed method on both public and realworld datasets encompassing a total of seven LiDAR sensors. The results demonstrate that DVMM significantly outperforms state-of-the-art descriptors in handling multi-modal LiDAR data and is compatible with collaborative SLAM systems. The code will be open-sourced upon acceptance.