Feature matching is often used to calculate pose information in visual measurement, but there is no available algorithm for designing feature matching for infrared active targets. In order to achieve matching of infrared active targets with different distributions, this paper proposes a general two-stage feature Point matching method. The first stage is coarse registration. First, the convex hull of the image feature point set is detected to obtain the outermost points. Fast coarse registration is achieved by constructing a triangle feature set and using Mahalanobis distance to calculate and search for similar triangles. The second stage is precise matching. First, the Euler angle is calculated through coarse matching features to avoid the 180° rotational symmetry of the matching results. In order to solve the problem of possible missing feature points after coarse registration, the epipolar constraint fine matching strategy is adopted to make full use of the existing features. Match the geometric information of feature points to effectively achieve accurate matching of remaining points. Theoretical analysis and experiments show that under the rotational symmetry point set and the non-rotation symmetry point set composed of 13 infrared luminescent points, this method can efficiently match within the absolute rotation range of 0°~40°, and the experimental test limit performance can reach 50°, and has good robustness to the occlusion of feature points in actual scenes. The experimental results verify its adaptability and stability, and has high practical value.