goldbattle
commented
1 year ago Does your IMU measure gravity / are you using the "raw" IMU readings? It looks like the bspline isn't able to recover the accelerations (see 3rd page of the report).
On Tue, Sep 13, 2022 at 4:32 AM lenardxu @.***> wrote:
Greetings! Currently I am taking advantage of your shared tool kalibr_calibrate_imu_camera to calibrate my visual(mono)-inertial(external)-sensor. However, after various tries and experiments, the results are still not realistic. The following link to the pdf is the best estimated result so far.
https://drive.google.com/file/d/1fPdSuu1gwYxdce8BECoUQiCGtcx54IpY/view?usp=sharing And the corresponding bag's link: https://drive.google.com/file/d/1OvPiSmlS2xILy9msvLfntwvBX0g6NFD8/view?usp=sharing For comparison, here is the link to the pdf whose result is worse.
https://drive.google.com/file/d/1sPRBIfmXFu6L8mLuUB7YIgLl9cF9E140/view?usp=sharing And the corresponding bag's link: https://drive.google.com/file/d/1LTiClE8NKyDpHmbXBfiq6tKcszYtYENH/view?usp=sharing
The corresponding setup is: I use OAK-D camera which doesn't support hardware/external trigger and only the left mono one is used, and BNO055 as an external Imu sensor. As a result, a software synchronization other than hardware synchronization is adopted for syncing both messages. The software sync in general performs linear interpolation upon the temporally nearest two Imu data at the instant when an image is captured, in order to acquire the approxiamted Imu data at that instant. The Imu sensor coordinate frames after validation is aligned with the camera's, i.e., z pointing to forward, x to right and y to downward. And the Imu's origin with respect to the camera's is roughly about (x, y, z) = (+3cm, +2.6cm, +1.5cm).
The Imu sensor is self-calibrated and further calibrated in terms of its noise parameters using allan_variance_ros tool recommended by you. And the camera sensor is re-calibrated using your multi camera calibration tool, which turns out to be good given the low reprojection error equal to around [-0.000000, 0.000000] +- [0.174857, 0.159922]pixel, or see the following:
Calibration results
Camera-system parameters: cam0 (/oakd_bno055_ros/left/image): type: <class 'aslam_cv.libaslam_cv_python.DistortedPinholeCameraGeometry'> distortion: [ 0.0750117 -0.17704149 0.00107357 -0.00171022] +- [0.01120927 0.02042603 0.00187764 0.00152155] projection: [429.79984321 430.77618923 324.10110114 198.35165358] +- [3.93954063 3.92397601 2.33407519 2.83935374] reprojection error: [-0.000000, 0.000000] +- [0.174857, 0.159922]
Target configuration
Type: aprilgrid Tags: Rows: 6 Cols: 6 Size: 0.024 [m] Spacing 0.0072 [m]
In my experiment corresponding to the result shown above, the white noise is doubled and bias walk multiplied by ten upon the result of allan_variance_ros in order to consider the temperature. As you see in the following link:
https://drive.google.com/file/d/1AdeAJZLSV5uch3bvQYOWFxo3MTUmmYDV/view?usp=sharing
The calibartion target in calibration of vi-sensor is the same as above in calibration of multiple-camera.
Could anyone give me any pointer how to improve the result? Many thanks in advance.
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lenardxu
Greetings! Currently I am taking advantage of your shared tool
kalibr_calibrate_imu_camerato calibrate my visual(mono)-inertial(external)-sensor. However, after various tries and experiments, the results are still not realistic. The following link to the pdf is the best estimated result so far. https://drive.google.com/file/d/1fPdSuu1gwYxdce8BECoUQiCGtcx54IpY/view?usp=sharing And the corresponding bag's link: https://drive.google.com/file/d/1OvPiSmlS2xILy9msvLfntwvBX0g6NFD8/view?usp=sharing For comparison, here is the link to the pdf whose result is worse. https://drive.google.com/file/d/1sPRBIfmXFu6L8mLuUB7YIgLl9cF9E140/view?usp=sharing And the corresponding bag's link: https://drive.google.com/file/d/1LTiClE8NKyDpHmbXBfiq6tKcszYtYENH/view?usp=sharingThe corresponding setup is: I use OAK-D camera which doesn't support hardware/external trigger and only the left mono one is used, and BNO055 as an external Imu sensor. As a result, a software synchronization other than hardware synchronization is adopted for syncing both messages. The software sync in general performs linear interpolation upon the temporally nearest two Imu data at the instant when an image is captured, in order to acquire the approxiamted Imu data at that instant. The Imu sensor coordinate frames after validation is aligned with the camera's, i.e., z pointing to forward, x to right and y to downward. And the Imu's origin with respect to the camera's is roughly about (x, y, z) = (+3cm, +2.6cm, +1.5cm).
The Imu sensor is self-calibrated and further calibrated in terms of its noise parameters using
allan_variance_rostool recommended by you. And the camera sensor is re-calibrated using your multi camera calibration tool, which turns out to be good given the low reprojection error equal to around [-0.000000, 0.000000] +- [0.174857, 0.159922]pixel, or see the following:In my experiment corresponding to the result shown above, the white noise is doubled and bias walk multiplied by ten upon the result of
allan_variance_rosin order to consider the temperature. As you see in the following link: https://drive.google.com/file/d/1AdeAJZLSV5uch3bvQYOWFxo3MTUmmYDV/view?usp=sharingThe calibartion target in calibration of vi-sensor is the same as above in calibration of multiple-camera.
Could anyone give me any pointer how to improve the result? Many thanks in advance.