The positioning accuracy of the mobile laser scanning (MLS) system can reach the level of centimeter under the conditions where GPS works normally. However, in GPS-denied environments this accuracy can be reduced to the decimeter or even the meter level because the observation mode errors and the boresight alignment errors of MLS cannot be calibrated or corrected by the GPS signal. To bridge this research gap, this paper proposes a novel technique that appropriately incorporates the robust weight total least squares (RWTLS) and the full information maximum likelihood optimal estimation (FIMLOE) to improve the positioning accuracy of the MLS system under GPS-denied environment. First of all, the coordinate transformation relationship and the observation parameters vector of MLS system are established. Secondly, the RWTLS algorithm is used to correct the 3D point observation model; then the uncertainty propagation parameter vector and the boresight alignment errors between the laser scanner frame and the IMU frame are calibrated by FIMLOE. Lastly, experimental investigation in indoor scenarios was performed to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method is able to significantly improve the positioning accuracy of an MLS system in GPS-denied environments.