Earlier quoted context omitted.
>mostly photogrammetry - being able to tell the position and orientation of your mirrors from a video stream - in a feedback loop with motor controllers and temperature sensors. This. Modern photogrammetry uses computer vision techniques like deep neural nets. It's an amazing advancement. Imagine this problem: having to place a ground vehicle like a wheeled robot at an arbitrary coordinate in a room. The mirror posit…
> This. Modern photogrammetry uses computer vision techniques like deep neural nets. I'm surprised to hear that. What good do DNN do for photogrammetry? Aren't they too unreliable? I only have university course-level exposure to this, most of it spent learning how to compute coordinate systems and transformation matrices from points on a set of pictures taken from different position. I guess DNNs could help with obje…
* https://www.youtube.com/watch?v=Ccj1O7yndIk
* https://www.youtube.com/watch?v=eOuonMhEsxI
* 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: