A real-time global re-localization framework for 3D LiDAR SLAM

TL;DR

This paper introduces a real-time global re-localization framework for 3D LiDAR SLAM that uses a dense template matching approach with enhanced descriptors and a cascade matching method, achieving high accuracy and speed.

Contribution

It proposes a novel template matching framework with expanded descriptors and an efficient cascade matching method for real-time global re-localization in 3D LiDAR SLAM.

Findings

Achieves 0.2-meter localization accuracy at 10Hz.

Uses 100k templates for robust re-localization.

Effective in real-time SLAM applications.

Abstract

Simultaneous localization and mapping (SLAM) has been a hot research field in the past years. Against the backdrop of more affordable 3D LiDAR sensors, research on 3D LiDAR SLAM is becoming increasingly popular. Furthermore, the re-localization problem with a point cloud map is the foundation for other SLAM applications. In this paper, a template matching framework is proposed to re-localize a robot globally in a 3D LiDAR map. This presents two main challenges. First, most global descriptors for point cloud can only be used for place detection under a small local area. Therefore, in order to re-localize globally in the map, point clouds and descriptors(templates) are densely collected using a reconstructed mesh model at an offline stage by a physical simulation engine to expand the functional distance of point cloud descriptors. Second, the increased number of collected templates makes the matching stage too slow to meet the real-time requirement, for which a cascade matching method is presented for better efficiency. In the experiments, the proposed framework achieves 0.2-meter accuracy at about 10Hz matching speed using pure python implementation with 100k templates, which is effective and efficient for SLAM applications.