Robust point cloud registration based on topological graph and Cauchy weighted l q –norm

本文提出了一种基于拓扑图和Cauchy加权lq范数的鲁棒有效点云配准方法WlqICP。在模拟和真实数据上的大量实验证明了WlqICP法更稳健（能够容忍高达99%的异常值），并且比最先进的方法效率更高。This paper proposes a robust and efficient point cloud registration method WlqICP based on topological graphs and Cauchy-weighted lq norm. Extensive experiments on both simulated and real-world datasets demonstrate that WlqICP exhibits greater robustness (capable of tolerating up to 99% of outliers) and higher efficiency compared to state-of-the-art methods.

搜集汇总

背景与挑战

背景概述

该数据集提出了一种基于拓扑图和Cauchy加权lq范数的鲁棒点云配准方法WlqICP，实验表明其能有效处理高达99%的异常值且效率优于先进方法。作为人工林资源监测关键技术研究的一部分，它以文档形式提供，属于研究报告集。