In the past few years,robots have become an important means of substation inspection,and robotic inspection technology for non-fixed lines has received increasing attention in order to perform inspection tasks more flexibly.How to achieve high-precision positioning in complex substation environment is one of the core problems to be solved.It is difficult for a single sensor to meet the requirements of reliable positioning in substations,therefore,this paper designs a multi-sensor fusion LINS-GNSS positioning method.Its front-end tightly couples LiDAR and inertial navigation based on an iterative error-state Kalman filter framework,which recursively corrects the estimated state by generating new feature correspondences in each iteration.The back-end uses a factor graph optimization approach to loosely couple the localization results from the satellite navigation with the localization results output from the LINS back-end.The optimization process first aligns the local coordinate system with the global coordinate system,then adds the position constraints of the GNSS as a priori edge to the factor graph in the back-end,and finally outputs the positioning results in the global coordinate system.In order to evaluate the performance of the LINS-GNSS system in the substation environment,this paper conducted field tests under real scenarios.The experimental results show that the LINS-GNSS system can achieve a positioning accuracy better than 0.5 m in the substation environment,better than LIO-SAM.