电子测量与仪器学报

张绍荣,张闻宇,李云,李智,周巧文.基于 FAST 角点和 FREAK 描述符改进的无人机景象匹配算法[J].电子测量与仪器学报,2020,34(4):102-110

基于 FAST 角点和 FREAK 描述符改进的无人机景象匹配算法

Improved UAV scene matching algorithm based on FAST corner and FREAK descriptor

DOI：

英文关键词:UAV scene matching FAST corner FREAK descriptor

基金项目:国家自然科学基金(6186010041)资助项目

作者	单位
张绍荣	1. 桂林航天工业学院电子信息与自动化学院，2. 桂林电子科技大学电子工程与自动化学院
张闻宇	2. 桂林电子科技大学电子工程与自动化学院
李云	1. 桂林航天工业学院电子信息与自动化学院
李智	1. 桂林航天工业学院电子信息与自动化学院，2. 桂林电子科技大学电子工程与自动化学院
周巧文	1. 桂林航天工业学院电子信息与自动化学院

Author	Institution
Zhang Shaorong	1. College of Electronic Information and Automation,Guilin University of Aerospace Technology，2. College of Electronic Engineering and Automation,Guilin University of Electronic Technology
Zhang Wenyu	2. College of Electronic Engineering and Automation,Guilin University of Electronic Technology
Li Yun	1. College of Electronic Information and Automation,Guilin University of Aerospace Technology
Li Zhi	1. College of Electronic Information and Automation,Guilin University of Aerospace Technology，2. College of Electronic Engineering and Automation,Guilin University of Electronic Technology
Zhou Qiaowen	1. College of Electronic Information and Automation,Guilin University of Aerospace Technology

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中文摘要:

在无基准图的无人机返航过程中,实时图和航路点的景象匹配是无人机返航成功的关键。为提高景象匹配的实时性和鲁棒性,提出了基于加速分割检测特征(FAST)角点检测和快速视网膜关键点(FREAK)描述符的无人机景象匹配算法。首先, 针对 FAST 角点检测方法的无尺度不变性、特征点数量冗余的缺点进行了改进;接着,对 FREAK 二进制描述符进行简化,以提高匹配速度;然后,采用 K 近邻比值法和 RANSAC 方法进行特征的初匹配和精匹配,并建立定位模型,从而获得航路点与无人机当前位置的实际距离和方位信息;最后,对算法的各项性能做实验验证。所提出的算法定位方向偏差在 1°以内,像面距离偏差稳定在 0. 6 pixel,运行时间 0. 43 s,远小于尺度不变特征转换(SIFT)和加速鲁棒特征(SURF)算法的处理时间。在尺度变换和噪声等条件变化的情况下,相比 SIFT 和 SURF 等算法,所提算法取得了较好的正确匹配率,具有更好的鲁棒性。实验结果表明所提出的算法鲁棒性好,运算速度快,尤其在视角变换方面表现优秀,更适合无人机视觉辅助导航。

英文摘要:

In the process of unmanned aerial vehicle (UAV) return without a reference map, the scene matching between the real-time map and the waypoint is the key to the success of the UAV return. In order to improve the real-time and robustness of scene matching, a UAV scene matching algorithm based on FAST corner detection and FREAK descriptor is proposed. Firstly, in order to improve the shortcomings of FAST corner detection method such as no scale invariance and redundant feature points, a multi-scale gridded feature detection method based on FAST corner is proposed. Next, the FREAK binary descriptor is simplified to improve the matching speed. Then, the K-nearest neighbor ratio method and RANSAC method are used for the initial and fine matching of the features, and a positioning model is established to obtain the actual distance between the waypoint and the current position of the UAV and orientation information. Finally, experiments are performed to verify the performance of the algorithm. The deviation of the positioning direction of the proposed algorithm is within 1 °, and the deviation of the image plane distance is stable within 0. 6 pixels, the running time is 0. 43 s, which is much shorter than the processing time of SIFT and SURF algorithms. In the case of conditions such as scale transformation and noise, compared with algorithms such as SIFT and SURF, the proposed algorithm has achieved a better correct matching rate and better robustness. The experimental results show that the proposed algorithm is robust and fast, especially in perspective transformation, it is more suitable for UAV vision-assisted navigation.

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