# Visual-LiDAR Odometry and Mapping with Monocular Scale Correction and Visual Bootstrapping

arXiv: https://arxiv.org/abs/2304.08978 · HTML: https://mlsanity.com/paper/2304.08978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [F-LOAM: Fast LiDAR Odometry And Mapping](https://mlsanity.com/paper/2107.00822/) (2021)
- [CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure](https://mlsanity.com/paper/2109.12979/) (2021)
- [BALM: Bundle Adjustment for Lidar Mapping](https://mlsanity.com/paper/2010.08215/) (2020)
- [LIMO: Lidar-Monocular Visual Odometry](https://mlsanity.com/paper/1807.07524/) (2018)
- [CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System](https://mlsanity.com/paper/2011.11357/) (2020)
- [Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots](https://mlsanity.com/paper/1710.06230/) (2017)

## Abstract (arXiv)

This paper presents a novel visual-LiDAR odometry and mapping method with low-drift characteristics. The proposed method is based on two popular approaches, ORB-SLAM and A-LOAM, with monocular scale correction and visual-bootstrapped LiDAR poses initialization modifications. The scale corrector calculates the proportion between the depth of image keypoints recovered by triangulation and that provided by LiDAR, using an outlier rejection process for accuracy improvement. Concerning LiDAR poses initialization, the visual odometry approach gives the initial guesses of LiDAR motions for better performance. This methodology is not only applicable to high-resolution LiDAR but can also adapt to low-resolution LiDAR. To evaluate the proposed SLAM system's robustness and accuracy, we conducted experiments on the KITTI Odometry and S3E datasets. Experimental results illustrate that our method significantly outperforms standalone ORB-SLAM2 and A-LOAM. Furthermore, regarding the accuracy of visual odometry with scale correction, our method performs similarly to the stereo-mode ORB-SLAM2.