DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition

Abstract

LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use the raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to divide the point cloud into several segments and extract the features of the segments from both spatial distribution and shape differences. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods.