Deep Place Recognition for 3D LiDAR Point Cloud with Transformer

Abstract

3D point cloud-based place recognition has gotten more attention since 3D LiDAR sensors are widely used for robotic applications and autonomous driving. Most of the existing deep point cloud-based methods take a few regular number points or image-like formats as inputs which are inability to make full use of point clouds' geometric information. This paper proposes a novel place recognition approach that is flexible and effective to handle diverse numbers of 3D LiDAR point clouds in large-scale environments. The approach is composed of feature extraction and a global descriptor encoding. The feature extraction consumes the 3D LiDAR point cloud with KPConv that can extract features efficiently and flexibly. Before the global descriptor encoding, a transformer module is employed to aggregate the contextual information that exploits the relationship of all features. The NetVLAD layer encodes the features into a global descriptor for recognizing a similar place rapidly. The proposed approach is evaluated on the KITTI odometry dataset, which demonstrates the validity of the proposed approach.