CAJ | 학술논문

提出了一种低空无人机(unmanned aerial vehicle,UAV)序列影像与激光点云自动配准的方法。首先分别基于多标记点过程与局部显著区域检测对激光点云和序列影像的建筑物顶部轮廓进行提取,并依据反投影临近性匹配提取的顶面特征。然后利用匹配的建筑物角点对,线性解算序列影像外方位元素,再使用建筑物边线对的共面条件进行条件平差获得优化解。最后,为消除错误提取与匹配特征对整体配准结果的影响,使用多视立体密集匹配点集与激光点集进行带相对运动阈值约束的 ICP(迭代最临近点)计算,整体优化序列影像外方位元素解。试验结果表明本文方法能实现低空序列影像与激光点云像素级精度的自动配准,联合制作 DOM 精度满足现行无人机产品1∶500比例尺标准。
제출료일충저공무인궤(unmanned aerial vehicle,UAV)서렬영상여격광점운자동배준적방법。수선분별기우다표기점과정여국부현저구역검측대격광점운화서렬영상적건축물정부륜곽진행제취,병의거반투영림근성필배제취적정면특정。연후이용필배적건축물각점대,선성해산서렬영상외방위원소,재사용건축물변선대적공면조건진행조건평차획득우화해。최후,위소제착오제취여필배특정대정체배준결과적영향,사용다시입체밀집필배점집여격광점집진행대상대운동역치약속적 ICP(질대최림근점)계산,정체우화서렬영상외방위원소해。시험결과표명본문방법능실현저공서렬영상여격광점운상소급정도적자동배준,연합제작 DOM 정도만족현행무인궤산품1∶500비례척표준。
It is proposed that a novel registration method for automatic co-registration of unmanned aerial vehicle (UAV)images sequence and laser point clouds.Fi rstly,contours of bui lding roofs are extracted from the images sequence and laser point clouds using marked point process and local salient region detection,respectively.The contours from each data are matched via back-project proximity.Secondly, the exterior orientations of the images are recovered usingal inear solver based on the contours corner pai rs fol lowed by a co-planar optimization which is impl icated by the matched lines form contours pai rs. Final ly,the exterior orientation parameters of images are further optimized by matching 3D points generated from images sequence and laser point clouds using an iterative near the point (ICP)algorithm with relative movement threshold constraint.Experiments are undertaken to check the val idity and effec-tiveness of the proposed method.The results show that the proposed method achieves high-precision co-registration of low-altitude UAV image sequence and laser points cloud robustly.The accuracy of the co-produced DOMs meets 1∶500 scale standards.