# An Integrated LiDAR-SLAM System for Complex Environment with Noisy Point Clouds

arXiv: https://arxiv.org/abs/2212.05705 · HTML: https://mlsanity.com/paper/2212.05705/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2022.

## Built on by (most cited first, 5 of 5)

- [Hyperbolic Chamfer Distance for Point Cloud Completion and Beyond](https://mlsanity.com/paper/2412.17951/) (2024)
- [Learning-Based Defect Recognitions for Autonomous UAV Inspections](https://mlsanity.com/paper/2302.06093/) (2023)
- [Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast](https://mlsanity.com/paper/2303.06388/) (2023)
- [A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors](https://mlsanity.com/paper/2312.01262/) (2023)
- [A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing](https://mlsanity.com/paper/2312.02208/) (2023)

## Abstract (arXiv)

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.