最邻近曲面约束的近景光学影像与地面激光点云几何配准

提出一种以最邻近曲面为约束的近景光学影像与地面激光点云高精度配准方法。根据光学影像生成三维稀疏点云,以影像三维稀疏点邻近的激光点拟合的曲面为约束,结合共线条件方程建立影像三维稀疏点云与三维激光点云间变换模型,通过平差迭代解算实现光学影像与激光点云的高精度几何配准。该方法只需提供初始配准参数,无需对激光点云数据进行特征提取和分割,并且基于曲面约束有效地解决了两个点集之间难以精确确定同名点的问题。通过实际数据试验表明该方法能获得很好的配准精度。

Geometric registration of close-range optical image and terrestrial laser point cloud constrained by nearest surface

In this paper, a high-precision registration method based on the nearest surface for close-range optical image and terrestrial laser point cloud is proposed.Three-dimensional sparse point cloud is generated from optical images. Constrained by the local surface fitted by the laser points adjacent to the 3D sparse point of the image, a transformation model between the three-dimensional sparse point cloud and the three-dimensional laser point cloud is constructed by using collinear conditional equation. The high precision geometric registration of optical images and the laser point cloud is completed by iterative calculation. The method only needs initial registration parameter and does not need to perform feature extraction and segmentation on the laser point cloud data. In addition, the problem that it is difficult to accurately determine the correspondence points between two sets of points is solved effectively based on the surface constraint. The results of two sets of experimental data show that this method can effectively improve the accuracy of rigid registration algorithm, and can achieve higher registration accuracy.