Aiming at the key step of feature matching in SLAM, an image tracking and matching algorithm combining feature points and feature regions is proposed to solve the problem of easily losing image features and high mismatching rate in discontinuous texture environment where texture is rich and texture is missing alternately. Firstly, ORB algorithm and semi-dense direct method are used to extract feature points and feature regions respectively. Secondly, progressive uniform sampling (PROSAC) is used to eliminate the mismatched feature points of ORB algorithm, and the correct matching rate of feature points will be calculated. Finally, aiming at the serious problem of feature point loss in texture-missing environment, the correct matching rate of the feature point should be the judgement basis. As for the low-matching images, a semi-dense direct method should be used to track low-matching image based on the correct matching rate of feature points. At the same time, the tracking results are optimized nonlinearly to improve the accuracy and stability of feature area tracking. The experimental results show that the algorithm is suitable for discontinuous texture environment, and it can improve the accuracy of image matching on the conditions of rich texture and texture missing.