# RGB-L: Enhancing Indirect Visual SLAM using LiDAR-based Dense Depth Maps

arXiv: https://arxiv.org/abs/2212.02085 · HTML: https://mlsanity.com/paper/2212.02085/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2022.

## Built on by (most cited first, 2 of 2)

- [DSP-SLAM++: A Unified Framework for Multi-Class, High-Fidelity Object SLAM in the Wild](https://mlsanity.com/paper/2606.25953/) (2026)
- [Desc++: Efficient Descriptor Enhancement for Data Association in Existing Visual SLAM Systems](https://mlsanity.com/paper/2607.11099/) (2026)

## Abstract (arXiv)

In this paper, we present a novel method for integrating 3D LiDAR depth measurements into the existing ORB-SLAM3 by building upon the RGB-D mode. We propose and compare two methods of depth map generation: conventional computer vision methods, namely an inverse dilation operation, and a supervised deep learning-based approach. We integrate the former directly into the ORB-SLAM3 framework by adding a so-called RGB-L (LiDAR) mode that directly reads LiDAR point clouds. The proposed methods are evaluated on the KITTI Odometry dataset and compared to each other and the standard ORB-SLAM3 stereo method. We demonstrate that, depending on the environment, advantages in trajectory accuracy and robustness can be achieved. Furthermore, we demonstrate that the runtime of the ORB-SLAM3 algorithm can be reduced by more than 40 % compared to the stereo mode. The related code for the ORB-SLAM3 RGB-L mode will be available as open-source software under https://github.com/TUMFTM/ORB SLAM3 RGBL.