结合自适应关键帧策略与运动信息的特征匹配方法

Feature Matching Method Combining Adaptive Keyframe Strategy with Motion Information

针对视觉惯性导航系统在高动态场景下由于快速运动、成像模糊等导致特征匹配精度低的问题,提出一种结合自适应关键帧策略与运动信息的特征匹配方法。首先,基于时间、惯性运动、成像清晰度及视差4项因子构建关键帧的更新判据,提出一种自适应关键帧策略,以提升关键帧的选取质量。其次,根据惯性运动信息,通过对图像进行几何变换实现相邻关键帧图像之间的共视区域匹配,旨在增强共视区域特征的可检测性。然后,采用改进的Oriented FAST and Rotated BRIEF(ORB)特征方法进行特征点的提取与匹配,以提高视觉特征点的匹配精度。最后,在公开数据集EuRoC上对所提方法进行测试。实验结果表明,所提特征匹配方法具有较好的精度与鲁棒性,在光照变化、成像模糊等动态场景具有较好的实用价值。

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.