This paper presents a point cloud downsampling algorithm for fast and accurate trajectory optimization based on global registration error minimization. The proposed algorithm selects a weighted subset of residuals of the input point cloud such that the subset yields exactly the same quadratic point cloud registration error function as that of the original point cloud at the evaluation point. This method accurately approximates the original registration error function with only a small subset of input points (29 residuals at a minimum). Experimental results using the KITTI dataset demonstrate that the proposed algorithm significantly reduces processing time (by 87\%) and memory consumption (by 99\%) for global registration error minimization while retaining accuracy.

翻译：本文提出一种基于全局配准误差最小化的点云下采样算法，用于快速准确的轨迹优化。该算法从输入点云中选取加权残差子集，使得该子集在评估点处产生与原始点云完全相同的二次点云配准误差函数。该方法仅需少量输入点子集（最少29个残差）即可精确逼近原始配准误差函数。基于KITTI数据集的实验表明，所提算法在保持精度的同时，显著降低了全局配准误差最小化的处理时间（降低87%）和内存消耗（降低99%）。