Abstract: The keypoint algorithm, as a fundamental algorithm in machine vision, plays a crucial role in enhancing the visual perception capabilities of new mining equipment. The keypoint algorithm can be applied across various mining tasks. The unique characteristics of the mine environment, such as lighting variations, dust interference, lack of environmental texture, and repetitive texture structures, present significant challenges for keypoint algorithms. To effectively evaluate the applicability of keypoints in underground mine environments, this paper constructed two types of datasets. The first dataset was the mine coal wall image test dataset, containing 20 sets of challenging coal wall or tunnel wall image sequences, while the second was the tunnel inspection image dataset, recording 589 image frames from a wheeled robot during an inspection process. In comparative experiments, we evaluated various keypoint algorithms, including SIFT, ORB, SURF, AKAZE, L2-Net, HardNet, GeoDesc, SuperPoint, R2D2, and DISK. The experimental results show that deep learning-based keypoint algorithms exhibit superior overall performance, with R2D2 demonstrating significant advantages over other algorithms. Additionally, we evaluated the efficiency of deep learning-based keypoint algorithms on low-power edge computing platforms, further validating their feasibility in industrial applications.