基于阈值自适应调整的图像特征均匀分布ORB算法改进

Advanced ORB algorithm of image feature uniform distribution based on threshold adaptive

基于相机的无人驾驶汽车视觉同步定位与地图构建(SLAM),可完成无人驾驶汽车的定位与建图。针对传统ORB(Oriented FAST and Rotated BRIEF)算法在提取图像特征点时容易造成冗杂、分布集中的问题,提出一种限制四叉树算法分裂深度的改进ORB(A-ORB)算法。该算法构造图像金字塔解决尺度不变性问题;根据所提取的特征点总数计算出每层金字塔所需要提取的特征点数;对每层金字塔图像采用自适应区域划分,根据图像信息计算特征点提取阈值;利用改进四叉树算法来均匀化分布特征点。进行了模拟实验。结果表明:相较于ORB、MA以及S-ORB算法,该算法运行效率提高了30%以上,匹配精度提高了10%以上。

Camera-based visual SLAM(simultaneous localization and mapping)for driverless vehicles could be used to complete the localization and mapping of driverless vehicles.In order to solve the problems that the traditional ORB(Oriented FAST and Rotated BRIEF)algorithm could easily cause clutter and concentration of distribution when extracting image feature points,an advanced ORB(A-ORB)algorithm was proposed which limited the splitting depth of quadtree algorithm.This algorithm constructed an image pyramid to solve the scale invariance problem.According to the total number of feature points extracted,the algorithm calculated the feature points that needed to be extracted for each layer of the pyramid.The image of each pyramid layer was divided into adaptive regions and the threshold of feature point extraction was calculated according to the image information.This algorithm used improved quadtree algorithm to homogenize the distribution feature points.The simulation experiment was carried out.The results show that compared with ORB,MA and S-ORB algorithms,the running efficiency of the advanced ORB is increased by more than 30%,and the matching accuracy is increased by more than 10%.