无人直升机视觉着舰中合作目标角点检测算法  

Cooperative Object Corner Detection Algorithm for Visual Landing of Unmanned Helicopter

作　　者：郝帅[1] 程咏梅[1] 马旭[1] 赵建涛[1] 

机构地区：[1]西北工业大学自动化学院,陕西西安710072

出　　处：《西北工业大学学报》2013年第4期653-659,共7页Journal of Northwestern Polytechnical University

基　　金：航空科学基金(20100853010)资助

摘　　要：舰载无人直升机自主着舰视觉导航在拍摄图像时,存在大尺度、角度畸变,使得合作目标角点难以检测,提出一种基于SIFT的分区双向匹配角点检测算法。设计了一种非对称彩色合作目标(由红色背板、绿色的H形和三角形组成),利用色彩信息对合作目标进行分割裁剪、识别,并分别对基准裁剪图和实拍裁剪图提取SIFT特征。为了提高SIFT特征匹配的实时性和准确性,提出了分区双向匹配策略。首先求取基准和实拍裁剪图中H形、小三角形重心以及H形上距离三角形重心最近的边缘点,以这三对匹配点求取基准图和实拍图间的粗略仿射模型。将基准裁剪图中的SIFT特征点经过该模型变换得到实拍裁剪图中的映射点,以每个映射点为圆心,以裁剪图宽度的1/4为半径将其分区,匹配时只选择每个映射点区域内的SIFT特征匹配点。同理,对基准裁剪图也进行分区处理。然后通过双向匹配及RANSAC算法剔除错误的匹配对,利用正确的匹配对完成基准图和实拍图仿射变换的精确模型。最后,将基准图中标定好的角点经过仿射变换获取实拍图中合作目标的角点位置。实验结果表明,该算法不仅精度高、鲁棒性强,而且具有较好的实时性。It is difficult to detect the cooperative object comer of the real-time image shot by the visual navigation system of a carrier-based unmanned helicopter because of the serious distortion of scale and angle. Therefore we propose a partitioned and bidirectional corner matching algorithm based on the scale invariant feature transform （SIFT）. We design an asymmetric cooperative object which comprises red back, green H target and triangle. We use the color information of the cooperative object to segment and cut it from its background and then extract the SIFT features of a reference cutting image and a real-time cutting image respectively. We use the partitioned and bi- directional matching algorithm to improve the real time and accuracy of the SIFT feature matching. Firstly, we cal- culate the gravity centers of the H target and the triangle in the reference cutting image and the real-time cutting im- age and the edge points of the H target which are closest to the gravity center of the triangle. We use the three pairs of matching points to calculate the rough affine models of the reference cutting image and the real-time cutting im- age. Secondly, we use the models to transform the SIFT features of the reference cutting image, thus obtaining the mapping points of the real-time cutting image. We partition the mapping points by taking each mapping point as the central point of the circle and the 1/4 width of the real-time cutting image as the radius. We only match the SIFT features that are inside the region of mapping points. We use the above method to partition the reference cutting im- age. Then we use the bidirectional matching and the random sample consensus （RANSAC） algorithm to eliminate the wrong matching pairs. Thus we use the correct matching pairs to calculate the accurate affine model for the transformation between reference cutting image and real-time cutting image. Finally, the position of the cooperative object comer in the real-time cutting image is obtained by transforming the correct positi

关 键 词：算法 舰载机 设计 特征提取 直升机 图像匹配 图像处理 图像分割 不变性 着舰 导航 无人飞行器 尺度不变特征变换 视觉着舰 分区 双向匹配 

分 类 号：TP217.3[自动化与计算机技术—检测技术与自动化装置]