Automatically Identifying Uncertain Spherical Targets in 3D Point Clouds of Complex Scenario

In order to identify uncertain number of spherical targets with different radius from terrestrial laser scanning point clouds, a method of automatically locating spheres and estimating their radii is proposed.Firstly, local second-order surface fitting of scattered measured point set is used to estimate the embedded differential geometric properties on the specified position of the measured surface.Then the two main curvatures of every point are compared, and all points on probable spheres are roughly identified according to the curvature difference point by point.At the same time the corresponding spherical center coordinates and radii are estimated.We specially design hierarchical clustering algorithm for estimated spherical centers and radii, which is utilized to separate probable points into distinct spherical targets.Finally, every category of spherical surface points is used to approximate spherical surface of its target whose accurate geometric parameters are computed subsequently.The experimental results based on terrestrial laser scanning data of specially arranged scene demonstrate that, this method can not only pinpoint the center of spheres, but also robustly identify incomplete spherical surfaces occluded by obstacles.