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Fix issues with Index docs and toml file

This commit is contained in:
Mateo de Mayo 2022-07-20 17:54:44 -03:00
parent c141291a4e
commit 97b9ef7851
2 changed files with 13 additions and 13 deletions
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2
data/monado/index.toml
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@ -4,7 +4,7 @@
show-gui=1
# Ground-truth camera calibration used for simulation.
cam-calib="/home/mateo/Documents/apps/bsltdeps/basalt/data/index_calib_oxrimu.json"
cam-calib="/home/mateo/Documents/apps/bsltdeps/basalt/data/index_calib.json"
# Path to config file.
config-path="/home/mateo/Documents/apps/bsltdeps/basalt/data/euroc_config.json"

24
doc/monado/Vive.md
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@ -15,20 +15,20 @@ work very well.
The general calibration procedure is as follows:
1. Setup the calibration target:
1. Download [the one from kalibr](https://drive.google.com/file/d/1DqKWgePodCpAKJCd_Bz-hfiEQOSnn_k0/view)
2. Open the pdf file and measure with a ruler the black square side (e.g., 24 inch 1080p screen, 34% zoom in Okular viewer, gives 3cm)
3. Update aprlgrid_6x6.json "tagSize" property in meters (e.g., 3cm would be "0.03")
1. Download [the one from kalibr](https://drive.google.com/file/d/1DqKWgePodCpAKJCd_Bz-hfiEQOSnn_k0/view)
2. Open the pdf file and measure with a ruler the black square side (e.g., 24 inch 1080p screen, 34% zoom in Okular viewer, gives 3cm)
3. Update aprlgrid_6x6.json "tagSize" property in meters (e.g., 3cm would be "0.03")
2. Record camera calibration sequence with the euroc recorder in Monado:
- See calib-cam3 example from TUM-VI https://vision.in.tum.de/data/datasets/visual-inertial-dataset
- Or this example from ORB-SLAM3: https://www.youtube.com/watch?v=R_K9-O4ool8
- Note that for stereo calibration you want to cover as much as possible of
both cameras images but always trying to keep >90% of the calibration target
visible in both views. This can be a little tricky so practice it beforehand.
- See calib-cam3 example from TUM-VI https://vision.in.tum.de/data/datasets/visual-inertial-dataset
- Or this example from ORB-SLAM3: https://www.youtube.com/watch?v=R_K9-O4ool8
- Note that for stereo calibration you want to cover as much as possible of
both cameras images but always trying to keep >90% of the calibration target
visible in both views. This can be a little tricky so practice it beforehand.
3. Record camera-imu calibration sequence: (faster motions)
- Similar recommendations as in the previous sequence but this time we want
faster motions that excite all IMU axes, see these examples:
- calib-imu1 from TUM-VI https://vision.in.tum.de/data/datasets/visual-inertial-dataset
- Or this from ORB-SLAM3 https://www.youtube.com/watch?v=4XkivVLw5k4
- Similar recommendations as in the previous sequence but this time we want
faster motions that excite all IMU axes, see these examples:
- calib-imu1 from TUM-VI https://vision.in.tum.de/data/datasets/visual-inertial-dataset
- Or this from ORB-SLAM3 https://www.youtube.com/watch?v=4XkivVLw5k4
4. Now run camera calibration as explained [here](https://gitlab.freedesktop.org/mateosss/basalt/-/blob/xrtslam/doc/Calibration.md#camera-calibration)
5. Then camera+imu (no mocap) calibration as explained [here](https://gitlab.freedesktop.org/mateosss/basalt/-/blob/xrtslam/doc/Calibration.md#camera-imu-mocap-calibration)
6. If the datasets are not good, the calibration will likely not be very good as