基于平面靶标的多视点云对齐方法

在大型物体三维视觉测量中,多视点云的对齐是其关键技术之一.设计了一种带有多个间距已知的角点作为特征点的平面靶标.靶标置于视觉传感器在2个不同位置测量的公共区域,2次测得特征点三维坐标.用靶标上任意3个非共线特征点的三维坐标建立单位正交基,从而求得多视点云坐标系初始变换矩阵.以初始对齐后的对应特征点之间距离平方和建立目标函数,并引入距离控制来增加约束条件,以3点求得的坐标变换矩阵为初值,采用Levenberg-Marquardt优化方法解出最优的坐标变换矩阵.采用双目视觉传感器对一石膏像在2个位置进行了测量,实验结果表明该对齐方法简单可靠,优化后的对齐精度比优化前提高了约31%. 

Multi-view point clouds registration method based on planar target

Multi-view point clouds registration is one of the key techniques in 3D vision measurement for large object. A planar target with some corners as feature points was designed. The distances between those feature points were accurately known. The target was placed at the common region which was measured by a vision sensor at two different view-points, and the vision sensor measured the 3D coordinates of the feature points twice. By establishing unit orthogonal basis using 3 arbitrary non-colinear points on the target, the original coordinate frame transformation matrix for multi-view point clouds was found. An objective function for the sum of distances′ squares between the correspondent feature points was established after original registration, and the distances control was introduced to increase constraint. The optimal results were finally estimated by Levenberg-Marquardt optimization algorithm taking original coordinate frame transformation matrix as the initial value. A plaster model was measured by a binocular vision sensor at two positions. The experimental results show that the proposed registration method is simple and reliable, and the registration accuracy after optimizing is improved by about 31%.