Quatro++：在 LiDAR SLAM 回环闭合中利用地面分割的鲁棒全局配准
Quatro++: Robust Global Registration Exploiting Ground Segmentation for Loop Closing in LiDAR SLAM
AI总结：
本文提出 Quatro++ 全局配准框架，利用地面分割缓解 LiDAR 点云稀疏与对应关系退化问题，并提升 LiDAR SLAM 回环闭合的约束质量和建图精度。
摘要：
全局配准是一项基础任务，用于估计三维点云两个视点之间的相对位姿。然而，有两个问题会降低 LiDAR SLAM 中全局配准的性能：一个是稀疏性问题，另一个是退化问题。稀疏性问题由机械旋转式 LiDAR 传感器中三维点云测量的稀疏特性引起。退化问题有时会发生，原因是外点剔除方法拒绝了过多对应关系，导致剩余的内点少于三个。随着三维点云两个视点之间的位姿差异增大，这两个问题会变得更加严重。为解决这些问题，我们提出了一个鲁棒的全局配准框架，称为 Quatro++。在我们此前仅关注全局配准本身的工作基础上，本文从 LiDAR SLAM 中回环闭合的角度处理鲁棒全局配准。为此，我们利用地面分割来实现鲁棒的全局配准。通过实验，我们证明所提出的方法比最先进的全局配准方法具有更高的成功率，克服了稀疏性和退化问题。此外，我们表明地面分割显著有助于提高地面车辆的成功率。最后，我们将所提出的方法应用于 LiDAR SLAM 中的回环闭合模块，并证实回环约束的质量得到改善，呈现出更精确的建图结果。因此，实验证据支持我们的方法适合作为回环闭合中的初始对齐。我们的代码可在 https://quatro-plusplus.github.io 获取。
英文摘要：
Global registration is a fundamental task that estimates the relative pose between two viewpoints of 3D point clouds. However, there are two issues that degrade the performance of global registration in LiDAR SLAM: one is the sparsity issue and the other is degeneracy. The sparsity issue is caused by the sparse characteristics of the 3D point cloud measurements in a mechanically spinning LiDAR sensor. The degeneracy issue sometimes occurs because the outlier-rejection methods reject too many correspondences, leaving less than three inliers. These two issues have become more severe as the pose discrepancy between the two viewpoints of 3D point clouds becomes greater. To tackle these problems, we propose a robust global registration framework, called \textit{Quatro++}. Extending our previous work that solely focused on the global registration itself, we address the robust global registration in terms of the loop closing in LiDAR SLAM. To this end, ground segmentation is exploited to achieve robust global registration. Through the experiments, we demonstrate that our proposed method shows a higher success rate than the state-of-the-art global registration methods, overcoming the sparsity and degeneracy issues. In addition, we show that ground segmentation significantly helps to increase the success rate for the ground vehicles. Finally, we apply our proposed method to the loop closing module in LiDAR SLAM and confirm that the quality of the loop constraints is improved, showing more precise mapping results. Therefore, the experimental evidence corroborated the suitability of our method as an initial alignment in the loop closing. Our code is available at https://quatro-plusplus.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
