College of Transportation Science and Engineering
National Natural Science Foundation of China
In order to solve the problems in simultaneous localization and mapping (SLAM), such as insufficient localization accuracy, accumulation of error of matching feature points and long matching time of feature points, a fusion and improved RANSAC optical flow optimization algorithm was proposed. Based on the traditional RANSAC algorithm, the least square method was added to the model iterative optimization to estimate the optimal model, and the mismatching points of optical flow method were removed to reduce a large number of image mismatching feature points. The improved RANSAC optical flow method was fused with the feature points through Kalman filtering. Finally, the improved algorithm was used to perform SLAM localization accuracy experiments in the open EuRoC MAV data set. Experimental results show that the improved algorithm in this paper can effectively reduce the feature matching error of optical flow method, thus improving the positioning accuracy of UAV visual SLAM, which proves the effectiveness and feasibility of the improved algorithm.