Abstract: To address the problem that existing Simultaneous Localization and Mapping (SLAM) algorithms tend to suffer from localization drift or even failure in degenerated environments of underground coal mine roadways, a coal mine roadway SLAM algorithm incorporating degeneration factors was proposed. The algorithm was built on an Extended Kalman Filter (EKF) framework and integrated encoder information with data from an Inertial Measurement Unit (IMU) and LiDAR to achieve localization and mapping. The method for calculating the degeneration factor was improved by obtaining eigenvalues and degeneration factors through line and plane feature registration, where the magnitude of the degeneration factor characterized the degree of environmental degeneration, thereby enabling environmental degeneration assessment. A confidence fusion mechanism based on degeneration factors was designed to maintain high-precision localization and mapping while significantly enhancing system robustness. The residual was designed by increasing the weights of high-precision feature points and reducing those of low-precision ones, thereby improving the accuracy of degeneration factor characterization. Experimental results showed that, compared with existing algorithms such as the tightly coupled LiDAR-Inertial Odometry via Smoothing and Mapping (LIOSAM) and LiDAR Odometry and Mapping (LOAM), the proposed algorithm demonstrated stronger adaptability to degenerated coal mine environments and could stably complete localization and mapping tasks. The positioning error of the proposed algorithm in degenerated environments was 1.222 m, which was reduced by 26.506 m compared with the LIOSAM algorithm. In non-degenerated environments, its Root Mean Square Error (RMSE) averaged 0.116 m, which was lower than those of the LOAM and LIOSAM algorithms. The algorithm could also operate stably in roadway segments where features were degenerated .