  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.
