Abstract: Image matching is an extremely important part of simultaneous localization and mapping (SLAM) technology. It is used to determine camera position and posture based on the transformation relationship between images. The image matching method based on line features has strong robustness and noise resistance, making it more suitable for underground image matching. The line descriptors based on deep learning have high robustness to scenes such as line segment occlusion, and their performance is better than traditional descriptors. However, the descriptors of convolutional neural network architecture abstract variable length line segments into fixed dimensions for description, which is not conducive to matching images with large changes in line segment length and parallax. In order to solve the above problems, a line feature matching algorithm for mine images based on line segment detection and line transformers (LT) is proposed. The algorithm uses single parameter homomorphic filtering in the frequency domain to reduce the lighting component of the image, enhance the reflection component, and improve brightness and contrast. The algorithm uses contrast limited adaptive histogram equalization (CLAHE) algorithm in YUV space to balance brightness components and make brightness distribution more even. The algorithm transforms to RGB space to extract line segment detection (LSD) lines. A LT descriptor based on Transformer architecture is introduced to construct the feature vector of LSD lines, and finally complete line feature matching. The experimental results show that the algorithm combines the advantages of homomorphic filtering and CLAHE algorithm. After image enhancement, the brightness of the image is moderate, the contrast is good, the grayscale distribution is even. The enhancement effect is better than the single parameter homomorphic filtering algorithm and EnlightenGAN algorithm. The number of line features extracted by this algorithm has increased by an average of 32.92% compared to the original image. It has good robustness in matching underground images with different proportions of similar textures, varying degrees of rotation and translation changes. The average correct matching number is 61.75 pairs, with an average precision of 86.83%. It is superior to the line binary descriptor (LBD) algorithm, LBD_NNDR algorithm, and LT algorithm. It can meet the requirements of robust matching of mine images.