aruco_ekf_slam VS colmap

Most of the code in pytagmapper is geared towards building said map, using an algorithm called SLAM. Nowadays, SLAM is usually implemented by solving a numerical optimization problem. The pytagmapper project uses a variant called Gaussian Belief Propagation. Other more popular variants, are some form of EKF or Levenberg Marquardt. You can try using pytagmapper to build your map, or some other SLAM software. Googling "aruco tag slam" gives this ROS based project https://github.com/ydsf16/aruco_ekf_slam for example.

Not saying its easy to use, but there is an application gui and it is free: https://github.com/colmap/colmap

Colmap (Model folders (BIN and TXT), dense workspaces, NVM, PLY)

See colmap for example https://colmap.github.io/

You're looking into a problem called photogrammetry, and a well-studied one at that. I'd recommend looking into "shape from motion" (sfm); specifically techniques that do "dense reconstruction." I'd recommend COLMAP to start with. It does pose estimation from images (e.g. you point it at a bunch of images and it will figure out the relative poses of the cameras that took them), as well as sparse and dense reconstcution.

COLMAP builds dense meshes from a collection of cameras https://colmap.github.io/

I’ve seen references to using COLMAP (https://colmap.github.io/) to estimate camera position/pose, e.g. here

Classical photogrammetry, where I agree with u/tdgros that the way to go is https://colmap.github.io/. There are actually better variants in literature but nothing is more reliable and user-friendly than COLMAP. This will give you a very precise point cloud, that can be meshed if needed.