DLC-SLAM: A Robust LiDAR-SLAM System With Learning-Based Denoising and Loop Closure

The current light detection and ranging simultaneous localization and mapping (LiDAR-SLAM) 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 faced with specular surfaces such as glass, certain metals, and building walls that are rich in urban environments. Therefore, in this work, we propose a general LiDAR-SLAM system termed d enoising and l oop c losure (DLC-SLAM) to tackle the problem of denoising and loop closure in complex environments with many noises and outliers caused by reflective materials. Current approaches for point cloud denoising are mainly designed for small-scale point clouds and cannot be extended to large-scale point cloud scenes. In this work, we first proposed a lightweight network for large-scale point cloud 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 demonstrated by extensive experiments and benchmark studies that our proposed LiDAR-SLAM system has a significant boost in localization accuracy when faced with noisy point clouds, with a marginal increase in the computational cost. Our systematical implementations are open-sourced to benefit the community.