Look for Deep SLAM or visual odometry papers, such as: * https://www.youtube.com/watch?v=Ccj1O7yndIk https://www.youtube.com/watch?v=Ccj1O7yndIk * https://www

Look for Deep SLAM or visual odometry papers, such as: * https://www.youtube.com/watch?v=Ccj1O7yndIk https://www.youtube.com/watch?v=Ccj1O7yndIk * https://www.youtube.com/watch?v=eOuonMhEsxI https://www.youtube.com/watch?v=eOuonMhEsxI * https://www.groundai.com/project/df-slam-a-deep-learning-enhanced-visual-slam-system-based-on-deep-local-features/ https://www.groundai.com/project/df-slam-a-deep-learning-enh... The Related work section in the DF-SLAM paper is great. As you intuited, DNNs work great for tracking visual landmarks (better than traditional computed features) between frames. They can also estimate depth from a monocular camera, which is very useful. A more out-of-left-field idea is training a CNN to directly regress the camera pose (position and orientation) from an image: PoseNet, https://www.youtube.com/watch?v=u0MVbL_RyPU https://www.youtube.com/watch?v=u0MVbL_RyPU