Outdoor LiDAR SLAM algorithm integrating ground constraints and semantic information

Abstract

Mainstream LiDAR simultaneous localization and mapping (SLAM) systems often face challenges such as cumulative localization drift and loop closure mismatches when operating in large-scale complex outdoor environments, making it difficult to maintain global map consistency. This paper aims to propose a LiDAR SLAM system that integrates ground constraints with semantic information to comprehensively enhance robustness and localization accuracy in complex scenarios. First, to address pose estimation errors caused by complex terrain, a two-step ground segmentation strategy is proposed. This method obtains reliable ground parameters through coarse extraction and fine fitting, and introduces ground constraint factors into the backend pose graph optimization, effectively suppressing cumulative system drift. Second, addressing the issue of perceptual aliasing in traditional geometric loop closure detection within dynamic or structurally similar scenes, a coarse-to-fine two-stage loop closure detection method is proposed. The first stage uses Scan Context for rapid candidate frame retrieval, while the second stage integrates semantic topological features with geometric distributions for precise matching and verification, thereby eliminating false matches and calculating high-precision 6-DoF loop closure poses. Experimental results on a quadruped robot platform and the KITTI public data set demonstrate that the proposed method significantly reduces trajectory errors while maintaining real-time performance, showing superior performance in handling slopes, dynamic environments and large-scale loop closure scenarios. This work introduces a robust outdoor LiDAR SLAM framework that effectively combines ground constraints with a novel coarse-to-fine semantic loop closure detection method to resolve elevation drift and perceptual aliasing in complex environments.