This article presents a novel LT/PMTS/SINS hybrid measurement system for 6-DOF acquisition under dynamic conditions of large-scale metrology and a hybrid multisensor fusion strategy termed as suboptimal multiple fading adaptive fault-tolerant Kalman filter (SMFAFKF)/improved residual chi-square-eigenvalue (IRCS-E)/RTS is proposed for the enhancement of accuracy and dynamic performance. An SMFAFKF algorithm is studied for improving the state estimation accuracy and ensuring the continuous prediction and compensation for system pose errors during fault durations. In addition, an IRCS-E fault detection strategy is proposed to improve the fault detection sensitivity and accuracy for complex forms of dynamic failures. Furthermore, the RTS backward smoothing algorithm is utilized to reduce the cumulative error during observation update interval. Finally, experiments under different dynamic motion scenarios are carried out to evaluate the effectiveness of the proposed method, and the performance is discussed in comparison with related methods. The experimental results showed that based on the proposed method, the LT/PMTS/SINS system is capable of providing accurate and stable estimates of 6-DOF under various dynamic conditions with orientation RMSE below 0.027 and position RMSE below 0.21 mm.