Accurate mapping method for under-constrained LiDAR-inertial SLAM system under unstructured influence

HUANG Chenxuan; WANG Lei

doi:10.12438/cst.2025-0173

Abstract：Most Simultaneous Localization and Mapping(SLAM)systems that utilize tightly coupled laser-inertial odometry are often un-der-constrained in underground coal mines,leading to mapping failures and hindering the direct deployment of robots in operational front-lines.This paper analyzes the factors contributing to odometry drift in degraded environments,elucidating the mechanisms by which un-structured point clouds induce drift.We propose a targeted detection method for unstructured regions and a two-step denoising approach that includes filtering and interpolation to address dust and fog.An adaptive tightly coupled odometry system is developed,incorporating a factor that characterizes the state of unstructured areas.In a test environment reverse-engineered from the WHU-TLS Tunnel dataset,our system achieves an EAP-ERMS of approximately 0.40 m,an EAP-Mean of 0.37 m,a ERP-ERMS of 0.015 m,and an ERP-Mean of 0.011 m,outperforming other methods significantly.For engineering applications with Ultra-Wide Band devices,we use a nonlinear optimization method to maintain global odometry poses,constructing a factor graph with unstructured state constraints,UWB factors,and loop closure factors.By performing parallel global optimization on all factors,high-precision global localization and mapping are achieved.In a kilo-meter-scale simulated tunnel with UWB signals,the continuous mapping results show an EAP-ERMS of 1.48 m,an EAP-Mean of 1.32 m,an ERP-ERMS of 0.026 m,and an ERP-Mean of 0.013 m.In a 2 000 m-long,loop-free field test at Cuncaota Coal Mine,the EAP-ERMS was 15.64 m,the EAP-Mean was 14.53 m,the ERP-ERMS was 0.198 m,and the ERP-Mean was 0.037 m,demonstrating the system's potential for practic-al engineering applications.Our SLAM system,built with a 16-line LiDAR and a 6-axis IMU that meet explosion-proof standards,provides accurate mapping in degraded scenarios,significantly advancing the practical application of coal mine robots.