Factor graph optimization based solid state LiDAR fused IMU−UWB coal mine underground positioning approach

Accurate localization is an important foundation for r-ealizing unmanned driving, and SLAM (Simultaneous Localization and Mapping) is currently the mainstream localization technology. There are problems such as insufficient lighting, narrow and highly similar spatial features in coal mines. The SLAM method based on a single sensor is prone to environmental interference and feature degradation, making it difficult to meet the precise positioning requirements in coal mines. To address the aforementioned issues, a solid-state LiDAR integrated IMU−UWB downhole positioning method based on factor graph optimization was proposed and validated through experiments. Firstly, based on the characteristics of the underground environment and sensors in coal mines, a multi-sensor fusion positioning system for unmanned vehicles is designed. Distributed solid-state LiDAR is selected as the main sensor, IMU and UWB are used as auxiliary sensors, and a sensor layout scheme is designed; Secondly, selecting factor graph optimization with strong adaptability and high fusion accuracy as the multi-sensor fusion method, a solid state LiDAR IMU−UWB multi-sensor fusion positioning model was established, and a comparison experiment of solid state LiDAR IMU−UWB multi-sensor fusion positioning was conducted using a publicly available dataset to preliminarily verify the feasibility, accuracy, stability, and environmental adaptability of the fusion positioning method; Finally, design and build a simulation scenario for underground tunnels, conduct simulation experiments for underground long straight tunnels and global tunnels, and verify the feasibility of the fusion positioning system in practical applications. The experimental results show that in the scenario of long straight tunnels, compared with traditional SLAM positioning methods such as LOAM and LIO−SAM, the APE mean of LIUO−SAM positioning method decreased by 40.84% and 13.32% respectively, demonstrating better positioning performance; In the global tunnel scene, compared with traditional LOAM and LIO−SAM positioning methods, the APE mean of LIUO−SAM positioning method decreased by 88.30% and 76.37% respectively, indicating good positioning performance and high environmental adaptability. Comprehensively, the designed multi-sensor fusion localization system for underground coal mine and the proposed factor graph optimization method possess the feasibility and environmental adaptability for the application in underground coal mine scenarios, which helps to promote the development of unmanned technology in underground coal mine.