GOSMatch: Graph-of-Semantics Matching for Detecting Loop Closures in 3D LiDAR data
Yachen Zhu, Yanyang Ma, Long Chen, Cong Liu, Maosheng Ye, Lingxi Li
Abstract
Detecting loop closures in 3D Light Detection and Ranging (LiDAR) data is a challenging task since point-level methods always suffer from instability. This paper presents a semantic-level approach named GOSMatch to perform reliable place recognition. Our method leverages novel descriptors, which are generated from the spatial relationship between semantics, to perform frame description and data association. We also propose a coarse-to-fine strategy to efficiently search for loop closures. Besides, GOSMatch can give an accurate 6-DOF initial pose estimation once a loop closure is confirmed. Extensive experiments have been conducted on the KITTI odometry dataset and the results show that GOSMatch can achieve robust loop closure detection performance and outperform existing methods.