A Lidar SLAM Algorithm Based on Improved LIO-SAM

Abstract

Simultaneous Localization and Mapping (SLAM) technology, widely employed for perception and localization, constitute an indispensable component for autonomous positioning and perception of unknown environments in unmanned vehicles and mobile robots. This paper employs a tightly coupled factor graph optimization method, building upon the LIO-SAM algorithm. Additionally, improvements are made by optimizing loop-closure detection factors using the Lidar-Iris method, providing it with relocalization capabilities. The proposed enhancements are validated using datasets and tested on outdoor environments with varying degrees of loop closures. The results indicate a significant improvement in positioning accuracy, making it more effective in map construction within unfamiliar environments.