Abstract: The working precision of mobile robots in coal mines seriously depends on the accuracy of simultaneous localization and mapping (SLAM) technology. There are some problems such as feature missing and poor lighting conditions in long and straight underground roadway. The problems lead to the failure of the laser odometer and visual odometer. The result limits the effective application of traditional SLAM method in coal mine roadway. At present, the research of the SLAM method mainly focuses on the multi-sensor fusion mapping method. There is a lack of research on the improvement of the mapping precision of the laser SLAM method. In order to solve the above problems, facing the mapping requirements of mobile robot in coal mine roadway, a 3D map construction method of coal mine roadway mobile robot based on integrated factor graph optimization is proposed. The method adopts the strategy of front-end construction and back-end optimization. The method designs a front-end point cloud registration module and a back-end construction method based on filtering and graph optimization. Therefore, the mapping result is more accurate and adaptable. The environmental degradation in coal mine long and straight roadway leads to the low registration precision of 3D laser point cloud. In order to solve the above problem, integrating iterative closest point (ICP) and normal-distributions transform (NDT) algorithms, taking into account the geometric characteristics and probability distribution characteristics of point clouds, an integrated front-end point cloud registration module is designed, which realizes the accurate registration of point clouds. Inview of the back-end optimization problem of 3D laser SLAM, the back-end construction method based on pose map and factor map optimization is studied. The factor map optimization model integrating ICP and NDT relative pose factors is constructed to accurately estimate the pose of the mobile robot. The performance of the proposed method of 3D map construction under different working conditions is verified by using the open dataset KITTI and the simulated roadway point cloud dataset. The experimental results on the open dataset KITTI show the following points. In terms of global consistency, this method has similar performance with the traditional A-LOAM method based on feature point matching and the LeGO-LOAM method based on plane segmentation and feature point extraction. It is superior to the other two methods in the local precision of mapping. The experimental results on the simulated roadway point cloud dataset show the following points. This method has significant advantages, through factor map optimization, a 3D map with high consistency can be obtained. The precision and robustness of 3D map construction of coal mine roadway are improved. The problems of the feature point missing and laser odometer failure in long straight underground roadway are solved.