基于融合改进RANSAC光流法的无人机视觉SLAM研究

Research on UAV Visual SLAM Based on Fusing Improved RANSAC Optical Flow Method

摘要:

针对同时定位与建图(SLAM)中存在定位精度不足、匹配特征点误差累积和特征匹配时间较长,提出了一种融合改进RANSAC光流法的优化算法。该方法基于传统RANSAC算法,加入最小二乘法对模型进行迭代优化来估计最优模型,对光流法的误匹配点进行剔除,大量减少图像误匹配特征点；把融合改进后的RANSAC光流法与特征点通过卡尔曼滤波进行融合,最后使用改进后的算法在公开的EuRoC MAV数据集中进行SLAM定位精度实验。实验结果表明:该改进算法能够有效减小光流法特征匹配的误差,从而提高无人机视觉SLAM的定位精度。

Abstract:

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 fusing improved RANSAC optical flow optimization algorithm is proposed. Based on the traditional RANSAC algorithm, the least square method was added to iteratively optimize the model 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. Then 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.