A Robust LiDAR SLAM Method for Underground Coal Mine Robot with Degenerated Scene Compensation

Simultaneous localization and mapping (SLAM) is the key technology for automation of intelligent mining equipment digitization environment. However, shotcrete surface symmetrical roadway in underground coal mines make light detection ranging (LiDAR) SLAM prone to degeneration, which leads failure mobile robot mapping. To address these issues, this paper proposes a robust LiDAR method detects compensates degenerated scenes by integrating inertial measurement unit (IMU) data. First, disturbance model used detect direction degree degeneration caused insufficient line plane feature constraints obtaining factor vector degeneration. Second, state divided into rotation translation. The pose obtained IMU pre-integration projected features then local map matching achieve two-step compensation. Finally, globally consistent implemented based on sliding window graph optimization. extensive experimental results show that proposed achieves better robustness than LeGO-LOAM LIO-SAM. absolute position root mean square error (RMSE) only 0.161 m, provides an important reference autonomous navigation safety inspection.