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

TL;DR

This paper introduces RGB-L, a method that enhances visual SLAM by integrating LiDAR-based dense depth maps into ORB-SLAM3, improving accuracy and reducing runtime in various environments.

Contribution

It presents a novel integration of LiDAR data into ORB-SLAM3 using two depth map generation methods, including a deep learning approach, and demonstrates significant accuracy and efficiency gains.

Findings

Achieves improved trajectory accuracy and robustness in different environments.

Reduces ORB-SLAM3 runtime by over 40%.

Provides open-source implementation for the proposed RGB-L mode.

Abstract

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.