SGLC: Semantic Graph-Guided Coarse-Fine-Refine Full Loop Closing for LiDAR SLAM

TL;DR

SGLC introduces a real-time, semantic graph-guided loop closing method for LiDAR SLAM that enhances loop detection and 6-DoF pose estimation by leveraging semantic and geometric features, improving accuracy and efficiency.

Contribution

The paper presents SGLC, a novel full loop closing approach combining semantic graph-guided detection with a coarse-fine-refine pose estimation scheme for LiDAR SLAM.

Findings

Outperforms state-of-the-art methods in accuracy and efficiency

Effectively integrates semantic and geometric features for loop closing

Enhances SLAM performance by eliminating accumulated errors

Abstract

Loop closing is a crucial component in SLAM that helps eliminate accumulated errors through two main steps: loop detection and loop pose correction. The first step determines whether loop closing should be performed, while the second estimates the 6-DoF pose to correct odometry drift. Current methods mostly focus on developing robust descriptors for loop closure detection, often neglecting loop pose estimation. A few methods that do include pose estimation either suffer from low accuracy or incur high computational costs. To tackle this problem, we introduce SGLC, a real-time semantic graph-guided full loop closing method, with robust loop closure detection and 6-DoF pose estimation capabilities. SGLC takes into account the distinct characteristics of foreground and background points. For foreground instances, it builds a semantic graph that not only abstracts point cloud representation for fast descriptor generation and matching but also guides the subsequent loop verification and initial pose estimation. Background points, meanwhile, are exploited to provide more geometric features for scan-wise descriptor construction and stable planar information for further pose refinement. Loop pose estimation employs a \mbox{coarse-fine-refine} registration scheme that considers the alignment of both instance points and background points, offering high efficiency and accuracy. Extensive experiments on multiple publicly available datasets demonstrate its superiority over state-of-the-art methods. Additionally, we integrate SGLC into a SLAM system, eliminating accumulated errors and improving overall SLAM performance. The implementation of SGLC will be released at https://github.com/nubot-nudt/SGLC.