Aiming at the shortcomings of traditional point cloud registration algorithms, a point cloud initial alignment method based on the differential evolution algorithm is proposed. By designing an appropriate fitness function and selecting a proper evolution strategy, the probability and speed of finding the globally optimal solution are improved. Finally, the point to plane iterative closest point is used to complete the precise registration. Experimental results prove that the speed and robustness of the algorithm put forward here are improved.

中文翻译：

---

基于改进差分进化算法的点云配准初始对齐

针对传统点云配准算法的不足，提出了一种基于差分进化算法的点云初始对齐方法。通过设计合适的适应度函数和选择合适的进化策略，可以提高找到全局最优解的概率和速度。最后利用点到面迭代最近点完成精确配准。实验结果证明，本文提出的算法的速度和鲁棒性都有所提高。