Running maplab 2.0 on monocular video with no IMU about maplab HOT 4 OPEN

Hi! Thanks for the interest, and sorry for the slow answer, I'm on vacation. Your biggest current issue is that you still need an odometry method (i.e. something to initialize the poses in the graph). You can afterwards use maplab for loop-closure, multisession mapping, and batch optimization.

The odometry method that we happen to provide alongside, is rovioli, which is VIO only. Have you tried Orbslam2 or DSO, for example? If you make either one of those work, you can come back and I can go into details about the multisession mapping.

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I did try orb_slam_2_ros with some success. How can I proceed with that for multisession mapping?

Thanks.

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Hi there, sorry that obviously this is not to provide any helpful answers. I just happened to read this issue and was so glad to see such an interesting application of visual SLAM. So were you using a camera stream from a video game? In that case, how would the camera intrinsics (ususally published via /camera/camera_info) be make available? Thanks a lot!

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I actually ran some game collected images (frames pulled from game footage looking at roughly the same area but moving around to give some different view angles) through COLMAP and it gave me some camera intrinsics which I put into the right format which was published via /camera/camera_info. You can read more about the settings here: https://colmap.github.io/cameras.html. It has been a while since I worked with it, but I think you can take what COLMAP outputs and insert it into a camera.yaml file. You would have to figure out which parameters to change, but see the format of the camera.yaml file below:

image_width: 640
image_height: 272
camera_name: "camera"
camera_matrix:
  rows: 3
  cols: 3
  data: [265.07249514822331, 0., 320, 0., 265.07249514822331, 136, 0., 0., 1.]
camera_model: "plumb_bob"
distortion_coefficients:
  rows: 1
  cols: 5
  data: [0, 0, 0, 0, 0]
rectification_matrix:
  rows: 3
  cols: 3
  data: [1, 0, 0, 0, 1, 0, 0, 0, 1]
projection_matrix:
  rows: 3
  cols: 4
  data:
    [
      265.07249514822331,
      0.,
      320,
      0.,
      0.,
      265.07249514822331,
      136,
      0.,
      0.,
      0.,
      1.,
      0.,
    ]

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