论文标题
一个综合的LiDAR-SLAM系统，用于具有嘈杂点云的复杂环境
An Integrated LiDAR-SLAM System for Complex Environment with Noisy Point Clouds
论文作者
论文摘要
当前面对复杂情况时，当前的激光雷达大满贯（同时定位和映射）系统遭受较低的精度和有限的鲁棒性。从我们的实验中，我们发现，当获得的点云中的噪声水平较大时，当前的激光雷达大满贯系统的性能有限。因此，在这项工作中，我们提出了一个通用框架，以解决在复杂环境中，在复杂的环境中，在具有反射材料引起的许多噪音和异常值的复杂环境中，LIDAR大满贯的循环闭合问题。当前的云云方法主要是为小规模点云而设计的，无法扩展到大规模点云场景。在这项工作中，我们首先提出了一个用于大规模点云的轻量级网络。随后，我们还设计了一个有效的循环封闭网络，以在全球优化中识别位置，以提高整个系统的本地化精度。最后，我们通过广泛的实验和基准研究证明，当面对嘈杂的点云时，我们的方法可以显着提高激光雷达大满贯系统的定位准确性，计算成本的边缘增加。
The current LiDAR SLAM (Simultaneous Localization and Mapping) system suffers greatly from low accuracy and limited robustness when faced with complicated circumstances. From our experiments, we find that current LiDAR SLAM systems have limited performance when the noise level in the obtained point clouds is large. Therefore, in this work, we propose a general framework to tackle the problem of denoising and loop closure for LiDAR SLAM in complex environments with many noises and outliers caused by reflective materials. Current approaches for point clouds denoising are mainly designed for small-scale point clouds and can not be extended to large-scale point clouds scenes. In this work, we firstly proposed a lightweight network for large-scale point clouds denoising. Subsequently, we have also designed an efficient loop closure network for place recognition in global optimization to improve the localization accuracy of the whole system. Finally, we have demonstrated by extensive experiments and benchmark studies that our method can have a significant boost on the localization accuracy of the LiDAR SLAM system when faced with noisy point clouds, with a marginal increase in computational cost.
