Pseudo Depth Maps for RGB-D SLAM

In visual SLAM, RGB-D cameras can actively obtain pixel depth information but are expensive and unsuitable for outdoor use. In contrast, monocular depth estimation is relatively inexpensive and more readily available. There have been some systems that fuse monocular depth estimation with SLAM algorithms, but no method that can directly generate depth maps has been tested with experiments. We propose that using monocular depth estimation results can generate 16-bit pseudo depth maps, which can be combined with monocular images as pseudo RGB-D. The relevant camera parameters can be configured according to the general RGB-D camera SLAM requirements. Experiments with Monodepth2 and ORB-SLAM3 on the KITTI dataset demonstrate that pseudo RGB-D can achieve as satisfactory results in SLAM as in stereo computation. The research paves the way for researchers can quickly examine monocular depth estimation results in more SLAM frameworks, reducing the cost of testing new depth monocular estimation frameworks.