# A Light-Weight LiDAR-Inertial SLAM System with Loop Closing

arXiv: https://arxiv.org/abs/2212.05743 · HTML: https://mlsanity.com/paper/2212.05743/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2022.

## Built on by (most cited first, 3 of 3)

- [Learning-Based Defect Recognitions for Autonomous UAV Inspections](https://mlsanity.com/paper/2302.06093/) (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)

In this work, we propose a lightweight integrated LiDAR-Inertial SLAM system with high efficiency and a great loop closure capacity. We found that the current State-of-the-art LiDAR-Inertial SLAM system has poor performance in loop closure. The LiDAR-Inertial SLAM system often fails with the large drifting and suffers from limited efficiency when faced with large-scale circumstances. In this work, firstly, to improve the speed of the whole LiDAR-Inertial SLAM system, we have proposed a new data structure of the sparse voxel-hashing to enhance the efficiency of the LiDAR-Inertial SLAM system. Secondly, to improve the point cloud-based localization performance, we have integrated the loop closure algorithms to improve the localization performance. Extensive experiments on the real-scene large-scale complicated circumstances demonstrate the great effectiveness and robustness of the proposed LiDAR-Inertial SLAM system.