Abstract: The automation and intelligent scheduling of coal mine auxiliary transport robots, along with high-precision, robust, real-time position sensing technology to ensure inherent safety and production efficiency, are key components of smart mine construction. However, due to the coupling interference of low light and weak textures underground, traditional measurement methods experience reduced accuracy or even failure. Especially in the process of constructing high-precision 3D images of roadways, the accuracy of pose estimation is significantly reduced, which seriously affects the stitching quality and global consistency of 3D point clouds of roadways. In order to solve the above problems, a real-time pose measurement method for roadway robots based on the Retinex principle and phase correlation analysis is proposed. In this algorithm, an adaptive discrimination module for low-light images based on image quality evaluation function is designed, which avoids the over-processing of normal images by Retinex. Retinexformer is used as the front-end enhancement module to improve the image clarity and contrast through the deep network, and enhance the perception ability of the algorithm in low-light environment. The image registration is realized by Fourier-Merlin transform (FMT) combined with phase correlation technology, which effectively compensates for the influence of image blur and illumination change caused by sensor motion on pose measurement, and greatly improves the accuracy and reliability of image registration. The real-time measurement of the position and posture of the robot body in the complex roadway environment is realized, which effectively improves the overall robustness and accuracy of the system. Several experiments have been carried out on the EuRoC public dataset and roadway scenes. The results show that compared with the existing measurement algorithms, the RMSE of MH04 and MH05 sequences under low light conditions is reduced by 28.57% and 26.94%, respectively. In order to further verify the generalization of the algorithm, the algorithm is tested in four modes, which shows higher estimation accuracy, real-time and robustness, and improves the pose measurement effect of the sensor in the low-light area of the roadway. Provides theoretical guidance and practical support for the realization of accurate, robust and lightweight autonomous positioning and roadway space reconstruction of coal mine auxiliary transportation robots.