This paper proposes a feature matching method that combines an adaptive keyframe strategy with motion information to address the problem that the feature matching accuracy of the visual inertial navigation system decreases due to blurred imaging and maneuvering in dynamic environments. First, we propose an adaptive keyframe strategy to improve the quality of keyframe selection by establishing an updating criterion for keyframes based on four indicators: time, inertial motion, imaging clarity, and parallax. Second, the common viewing region among adjacent keyframes is identified through geometric transformation of the image based on inertial motion to enhance feature detectability. Next, an improved Oriented FAST and Rotated BRIEF (ORB) feature method based on the Gaussian image pyramid is used to improve the matching accuracy of feature points. Finally, the performance of the proposed method is verified using EuRoC public datasets. The results show that the proposed method has better accuracy and robustness in applications with dynamic scenes, such as illumination changes and image blur.