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In this paper, we present a novel method for integrating 3D LiDAR depthmeasurements into the existing ORB-SLAM3 by building upon the RGB-D mode. Wepropose and compare two methods of depth map generation: conventional computervision methods, namely an inverse dilation operation, and a supervised deeplearning-based approach. We integrate the former directly into the ORB-SLAM3framework by adding a so-called RGB-L (LiDAR) mode that directly reads LiDARpoint clouds. The proposed methods are evaluated on the KITTI Odometry datasetand compared to each other and the standard ORB-SLAM3 stereo method. Wedemonstrate that, depending on the environment, advantages in trajectoryaccuracy and robustness can be achieved. Furthermore, we demonstrate that theruntime of the ORB-SLAM3 algorithm can be reduced by more than 40 % compared tothe stereo mode. The related code for the ORB-SLAM3 RGB-L mode will beavailable as open-source software under https://github.com/TUMFTM/ORB SLAM3RGBL.

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