The illumination conditions under black factory lights are poor, and the structural characteristics of some areas are similar. Hence, the TSR-LIO-SAM algorithm is proposed to improve the accuracy of the synchronous positioning and mapping algorithm of mobile robots under black factory lights. The LiDAR is tightly coupled with the inertial measurement unit (IMU), and a pre-integration factor is used to compensate the dynamic error of the IMU. The IMU high-frequency output is used to eliminate the LiDAR point cloud distortion. Simultaneously, a key frame selection and local mapping strategy based on the time-space threshold is proposed. The iterative closest point (ICP) algorithm is improved by combining the line-surface features and point cloud probability distribution, and the dynamic weight is configured to optimize the point cloud registration. Experimental results show that, compared with the LEGO-LOAM and LIO-SAM algorithms, the average error of the TSR-LIO-SAM algorithm is reduced by 77.96% and 9.77% respectively, and the root mean square error is reduced by 78.64% and 8.49% respectively, which prove the effectiveness of the TSR-LIO-SAM algorithm in indoor environments.