SparseARFM-SI: Rotary Point Cloud Place Recognition Based on Multi-Resolution and Attention Mechanism

Abstract

Place recognition based on 3D point cloud can directly describe the scene of 3D world by acquiring 3D point clouds via LiDAR, which is robust with environmental changes. The core challenge locates at how to obtain compact and representative feature expression for positioning. In this paper, SparseARFM-SI framework is proposed to solve the problem of feature extraction caused by the large size difference of different objects and the difficulty of feature extraction based on the point cloud data collected by rotating LiDAR. SparseARFM-SI is mainly composed of four models: data representation model, sparse convolution model, ARFM model and NetVLAD model. Experiments show that SparseARFM-SI framework has good performance in USyd data set collected based on rotating Lidar.