融合IMU去除运动模糊的改进光流匹配算法

In order to improve the accuracy and efficiency of the feature point matching, this paper proposed a novel feature point matching algorithm in terms of vision and inertial measurement unit (IMU) fusion. Firstly, the algorithm calculated the point diffusion function using the motion information of IMU to remove motion blur, and improved the feature point matching rate. Secondly, based on LK (Lucas-Kanade) optical flow method, this paper introduced gradient error and uses L1 parametric to simulate sparse noise. Furthermore, the feature point position by using IMU is the initial value of the algorithm, and then this paper used BB (Barzilar-Borwein) step to improve the efficiency of the algorithm. Finally, the comparison experiments show that the efficiency and accuracy of the algorithm are better than the LK optical flow method. Especially, the algorithm improves the localization accuracy and robustness of the VINS-Mono framework on the dataset EuRoC.