一种用于无人机室内定位的改进ORB光流算法

Improved ORB Feature Optical Flow Algorithm for Indoor Positioning of Unmanned Aerial Vehicle

针对无人机室内无GPS的导航定位问题,结合ORB特征与LK金字塔光流,提出了一种改进的ORB特征光流算法。采用ORB算法提取每帧图像的特征点,送入金字塔中估计下一帧点集坐标;采用前后双向单追踪策略进行粗匹配;采用FLANN-KNN匹配规则和前后双向双追踪策略组成的精匹配,进行误匹配点集的滤除。通过多种场景提取效果和无人机实际应用两部分实验,从实时性和精确性对算法性能进行了验证分析。仿真结果表明,改进算法具有较好的定位效果和较好的实时性。

In view of the navigation and positioning problem of Unmanned Aerial Vehicle(UAV)without GPS in rooms,an improved ORB feature optical flow algorithm is proposed,combined with ORB feature and LK pyramid optical flow.Firstly,the ORB algorithm is used to extract the feature points of each frame,and puts them into the pyramid to estimate the coordinates of these points in next frame.Secondly,the rough matching,a forward and backward tracking strategy,is carried out to filter these points.Finally,the fine matching,which is composed of the FLANN-KNN matching rule and two-way double tracking strategy,is used to filter out mismatched point sets.The algorithm performance is verified and analyzed from real-time and accuracy through the experiments,which include a variety of scene extraction effects and practical application of UAVs.The results of simulations show that the proposed improved algorithm has better positioning effect and better real-time performance.