With the development of unmanned driving technology， intelligent robots and drones， high-precision localization， navigation and state estimation technologies have also made great progress. Traditional global navigation satellite system / inertial navigation system（GNSS/INS） integrated navigation systems can provide high-precision navigation information continuously. However， when this system is applied to indoor or GNSS-denied environments， such as outdoor substations with strong electromagnetic interference and complex dense spaces， it is often unable to obtain high-precision GNSS positioning data. The positioning and orientation errors will diverge and accumulate rapidly， which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios. This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization. Through the collected experimental data and simulation results， this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.