基于球心点互斥的球目标识别方法

Spherical Target Recognition Method Based on Mutual Exclusion of Spherical Centers

提出了一种基于球心点互斥的球目标识别方法,用于从大场景三维点云中自动识别未知个数和未知半径的球目标。首先,根据专门设计的球面点响应函数滤除大量非球面点,并根据法向与曲率将剩余的球面点映射到球心位置;然后,构建用以描述局部密度渐变规律的球心点互斥树,通过剪枝操作将其分裂成若干子树,其分别对应不同球目标的球心点聚类;最后,根据球心点局部密度和球面点覆盖率估计值确认真实存在的球目标。实验结果表明:基于球心点互斥的球目标识别方法能够有效解决大场景三维点云中球目标的识别问题,即使是存在严重遮挡的情况下,暴露表面不足整个球面6%的球目标也都能够被识别出来。

A new spherical target recognition method based on mutual exclusion of sphere centers is proposed to solve the automatically identification problems of unknown number and unknown radius targets in large-scale 3D point clouds. First, an effective spherical point response function is specially designed to remove most of aspheric points, and every remaining spherical point is mapped to a sphere center by taking advantage of its normal and curvatures. Then, a novel tree-like structure for describing distribution and local density change rules of these centers is constructed, through a series of pruning operation complying with the mutually exclusion relationships between different sphere centers, the tree is split into several sub-trees, and a sub-tree correspond to a possible sphere target. Finally, the real sphere is confirmed by the local density of the root node of sub-tree and the coverage rate of points on the sphere surface. The experimental results demonstrate that the proposed sphere recognition method based on the mutual exclusion of sphere centers can effectively identify and precisely loc ate various spherical targets in a large and cluttered scene. Even in the case of serious occlusion, such as the exposed surface is less than 6%, the sphere can also be robustly identified.