An improved ORB-SLAM algorithm based on motion prediction was proposed by considering the influence of the camera’s own motion on the visual SLAM system aiming at the problem that the ORB-SLAM algorithm with fixed point feature extraction and matching strategy has large tracking and positioning error in different motion scenes. The point feature utilization rate of the previous frame and the uniform motion model were used to predict the mutually visual zone between two adjacent frames. The threshold of point feature extraction under different motion states was dynamically adjusted in real time. Then the accuracy of the system was improved while ensuring the stability of the system. A point feature matching optimization strategy based on motion prediction was proposed. The effective matching points within the mutually visual zone were quickly determined based on the uniform motion model. The matching search range was narrowed by combining the image pyramid in order to reduce many invalid matching processes. The comparison experiments were conducted on the TUM data set. Results show that the proposed algorithm not only has good real-time performance, but also improves the accuracy of the system.