Abstract: Addressing the urgent demand for autonomous positioning and environmental modeling technology in the context of intelligent mine development,and targeting the performance degradation of traditional SLAM algorithms caused by the unique underground environment in coal mines,this study innovatively proposes a mine positioning and mapping algorithm that fuses laser reflection intensity information. The algorithm is constructed based on feature point method laser SLAM framework,but it comes up with a novel classification method,which gets the intensity edge points using the changing rate of point cloud intensity. With the classical edge feature points and surface feature points based on point cloud curvature remain,the algorithm enriches the categories and quantities of point cloud features. Meanwhile,the algorithm constructs an intensity image of each frame based on its intensity data of point cloud, and tracks the image features using image processing technology, obtains more matched points. Furthermore,a modification of scan-matching procedure is made to import the intensity edge features and intensity image features,and improve the accuracy of pose estimation in underground coal mines. Experimental validation demonstrates that this method exhibits excellent stability and environmental adaptability in typical coal mine tunnel environments. Its positioning accuracy meets the requirements for underground applications,offering an effective solution to the problem of feature degradation underground.