摘要
提出了一种基于已知点和核面约束的近景摄影测量人工标志点分组匹配算法。首先利用定向棒点和编码标志点等已知点对所有像片进行分组;然后按三张一组进行组合,并计算各组合的几何质量;最后选择几何质量最好的部分像片进行组合,按核面约束进行匹配。实验证明,该算法匹配100余张像片1、0 000余像点的速度约为5 s,像点匹配率高于95%,误匹配率低于0.1‰,满足近景摄影测量的要求。
We present a cluster matching method of artificial targets in close range photogrammetry based on the known points and epipolar plane constraint.Firstly,all the images are grouped according to the known points such as points on the AutoBar and codes.Secondly,image triplets per known point and their geometrical quality are calculated.Finally,parts of the image triplets with best geometric goodness are selected to match following the epipolar plane constraint.The experimental results show that more than 95% image points of about 10 000 in round 100 pieces of images are matched in less than 5 s,with matching error rates less than 0.1‰.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2010年第8期979-982,共4页
Geomatics and Information Science of Wuhan University
基金
精密工程与工业测量国家测绘局重点实验室基金资助项目(PF2009-15)
关键词
近景摄影测量
人工标志点
匹配
几何质量
close range photogrammetry
artificial target
matching
geometrical quality
