  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.
