Summary:
- Using ATOM framework to perform full calibration of a mobile manipulator with two cameras, one on the AGV and other on the end-effector of the manipulator and one 3D lidar on the AGV.
- Aims to determine the transformations:
- Manipulator arm base w.r.t. the AGV.
- RGB1 w.r.t. the AGV.
- RGB2 w.r.t. the end-effector.
- 3D Lidar w.r.t. the AGV
- The cameras used was a depth astra.
- The 3D lidar is a velodyne VLP 16.
- The calibration used a total of 30 collections.
- The output is a URDF file with the optimized poses of the sensors.
Calibration tree and Transformations:
Videos:
Calibration Results per collection:
| Collection |
camera (px) |
camera_mb (px) |
velodyne (m) |
| 000 |
0.5561 |
0.5320 |
0.0064 |
| 001 |
0.5263 |
0.5386 |
0.0064 |
| 002 |
0.5308 |
0.5346 |
0.0065 |
| 003 |
0.4642 |
0.5373 |
0.0060 |
| 004 |
0.6165 |
0.5390 |
0.0066 |
| 005 |
0.4792 |
0.5398 |
0.0062 |
| 007 |
0.5577 |
0.7607 |
0.0064 |
| 008 |
0.5560 |
0.6584 |
0.0062 |
| 009 |
1.0303 |
0.6434 |
0.0062 |
| 010 |
0.7677 |
0.6300 |
0.0067 |
| 011 |
0.5588 |
0.6184 |
0.0064 |
| 012 |
0.4670 |
0.6080 |
0.0064 |
| 013 |
0.2949 |
0.4613 |
0.0064 |
| 014 |
0.3067 |
0.4478 |
0.0067 |
| 015 |
0.2968 |
0.2064 |
0.0063 |
| 016 |
4.0170 |
3.8441 |
0.0088 |
| 017 |
3.0238 |
3.0225 |
0.0086 |
| 018 |
0.4867 |
0.5048 |
0.0064 |
| 019 |
0.4108 |
0.6225 |
0.0069 |
| 020 |
0.3086 |
0.4979 |
0.0064 |
| 021 |
0.3161 |
0.4620 |
0.0062 |
| 022 |
0.3217 |
0.4307 |
0.0069 |
| 023 |
0.8256 |
0.4673 |
0.0062 |
| 024 |
0.4339 |
0.4116 |
0.0061 |
| 025 |
0.7071 |
0.6554 |
0.0065 |
| 026 |
0.5492 |
0.9245 |
0.0063 |
| 027 |
0.6525 |
1.0532 |
0.0062 |
| 028 |
0.9089 |
1.1664 |
0.0061 |
| 029 |
0.7902 |
1.1462 |
0.0063 |
| 030 |
0.7275 |
1.0891 |
0.0065 |
| Averages |
0.7496 |
0.8185 |
0.0065 |
Results from the simulation using noise initial guess nig 0.1 0.1
Ground truth frame evaluations using different datasets
rosrun atom_evaluation ground_truth_frame_evaluation -train_json /home/jorge/datasets/e6_mm/atom_calibration_nig_0.1_0.1.json -test_json /home/jorge/datasets/e6_mm/dataset_corrected.json
| frame # |
Xcal-Xgt (mm) |
Ycal-Ygt (mm) |
Zcal-Zgt (mm) |
Roll_cal-Roll_gt (deg) |
Pitch_cal-Pitch_gt (deg) |
Yaw_cal-Yaw_gt (deg) |
Average - Trans |
Average - Rot |
| base_link_mb_to_base_link |
9.2194 |
9.1957 |
17.4085 |
0.0491 |
0.0806 |
0.0806 |
21.7397 |
0.1924 |
| camera |
7.0570 |
20.7202 |
0.9205 |
0.0659 |
0.0101 |
0.0101 |
16.6333 |
0.3626 |
| camera_mb |
0.6239 |
22.4860 |
23.8324 |
0.0559 |
0.2354 |
0.2354 |
32.7719 |
0.4130 |
| velodyne |
1.7709 |
4.6873 |
26.4831 |
0.2674 |
0.0021 |
0.0021 |
26.9529 |
0.2849 |
Lidar to AGV camera evaluation (same dataset - Atom_calibration)
rosrun atom_evaluation lidar_to_rgb_evaluation -train_json /home/jorge/datasets/e6_mm/atom_calibration_nig_0.1_0.1.json -test_json /home/jorge/datasets/e6_mm/atom_calibration_nig_0.1_0.1.json -cs camera_mb -rs velodyne -si
| Collection # |
RMS (pix) |
X err (pix) |
Y err (pix) |
| 000 |
5.0165 |
3.1572 |
1.4601 |
| 001 |
5.5321 |
3.7744 |
1.4089 |
| 002 |
5.4894 |
3.9398 |
1.4221 |
| 003 |
3.6184 |
2.5606 |
1.0372 |
| 004 |
4.6210 |
3.5103 |
1.3770 |
| 005 |
2.6547 |
1.7478 |
0.7602 |
| 007 |
5.8273 |
3.5149 |
1.3806 |
| 008 |
4.9740 |
3.2375 |
1.3606 |
| 009 |
4.3480 |
2.8449 |
1.4139 |
| 010 |
6.3855 |
4.2268 |
1.5991 |
| 011 |
4.4527 |
3.0954 |
1.4694 |
| 012 |
4.8573 |
3.4109 |
1.4984 |
| 013 |
4.1894 |
2.8002 |
1.5317 |
| 014 |
4.3140 |
3.0491 |
1.4918 |
| 015 |
4.9254 |
3.1945 |
1.5025 |
| 016 |
2.1563 |
1.4797 |
0.5857 |
| 017 |
14.7812 |
3.3613 |
2.8118 |
| 018 |
22.8064 |
4.7569 |
5.0110 |
| 019 |
13.5416 |
4.4927 |
3.0352 |
| 020 |
4.2044 |
2.8963 |
1.3374 |
| 021 |
5.0278 |
3.3680 |
1.6414 |
| 022 |
14.8617 |
4.3641 |
4.0795 |
| 023 |
4.6806 |
3.5092 |
1.3324 |
| 024 |
5.2491 |
3.9192 |
1.3333 |
| 025 |
4.3652 |
2.9279 |
1.5166 |
| 026 |
5.1675 |
3.4426 |
2.0363 |
| 027 |
5.3017 |
3.4275 |
1.5963 |
| 028 |
4.1694 |
2.5320 |
1.9037 |
| 029 |
4.6223 |
3.0918 |
1.6882 |
| 030 |
4.3390 |
2.7611 |
1.8345 |
| Averages |
6.2160 |
3.2798 |
1.7486 |
Lidar to AGV camera evaluation (different dataset - Dataset_corrected vs Atom_calibration)
rosrun atom_evaluation lidar_to_rgb_evaluation -train_json /home/jorge/datasets/e6_mm/atom_calibration_nig_0.1_0.1.json -test_json /home/jorge/datasets/e6_mm/dataset_corrected.json -cs camera_mb -rs velodyne -si
| Collection # |
RMS (pix) |
X err (pix) |
Y err (pix) |
| 000 |
5.0165 |
3.1572 |
1.4601 |
| 001 |
5.5321 |
3.7744 |
1.4089 |
| 002 |
5.4894 |
3.9398 |
1.4221 |
| 003 |
3.6184 |
2.5606 |
1.0372 |
| 004 |
4.6210 |
3.5103 |
1.3770 |
| 005 |
2.6547 |
1.7478 |
0.7602 |
| 006 |
4.8705 |
3.3760 |
1.6965 |
| 007 |
5.8273 |
3.5149 |
1.3806 |
| 008 |
4.9740 |
3.2375 |
1.3606 |
| 009 |
4.3480 |
2.8449 |
1.4139 |
| 010 |
6.3855 |
4.2268 |
1.5991 |
| 011 |
4.4527 |
3.0954 |
1.4694 |
| 012 |
4.8573 |
3.4109 |
1.4984 |
| 013 |
4.1894 |
2.8002 |
1.5317 |
| 014 |
4.3140 |
3.0491 |
1.4918 |
| 015 |
4.9254 |
3.1945 |
1.5025 |
| 016 |
2.1563 |
1.4797 |
0.5857 |
| 017 |
14.7812 |
3.3613 |
2.8118 |
| 018 |
22.8064 |
4.7569 |
5.0110 |
| 019 |
13.5416 |
4.4927 |
3.0352 |
| 020 |
4.2044 |
2.8963 |
1.3374 |
| 021 |
5.0278 |
3.3680 |
1.6414 |
| 022 |
14.8617 |
4.3641 |
4.0795 |
| 023 |
4.6806 |
3.5092 |
1.3324 |
| 024 |
5.2491 |
3.9192 |
1.3333 |
| 025 |
4.3652 |
2.9279 |
1.5166 |
| 026 |
5.1675 |
3.4426 |
2.0363 |
| 027 |
5.3017 |
3.4275 |
1.5963 |
| 028 |
4.1694 |
2.5320 |
1.9037 |
| 029 |
4.6223 |
3.0918 |
1.6882 |
| 030 |
4.3390 |
2.7611 |
1.8345 |
| Averages |
6.1726 |
3.2829 |
1.7469 |
Eye-on-hand camera to AGV camera evaluation using different datasets (Dataset_corrected vs Atom_calibration)
rosrun atom_evaluation rgb_to_rgb_evaluation -train_json ~/datasets/e6_mm/atom_calibration_nig_0.1_0.1.json -test_json ~/datasets/e6_mm/dataset_corrected.json -ss camera -st camera_mb -si
| Collection # |
RMS (pix) |
X err (pix) |
Y err (pix) |
Trans (mm) |
Rot (deg) |
| 000 |
11.6271 |
3.1841 |
11.1411 |
21.7989 |
0.4879 |
| 001 |
11.2983 |
3.0497 |
10.8327 |
21.3140 |
0.6549 |
| 002 |
11.1305 |
3.2751 |
10.5919 |
21.2879 |
0.5673 |
| 003 |
10.9073 |
3.0797 |
10.4086 |
20.2596 |
0.5703 |
| 004 |
10.5472 |
2.7682 |
10.1234 |
19.7930 |
0.5297 |
| 005 |
10.8627 |
2.9775 |
10.3927 |
20.4319 |
0.2385 |
| 006 |
17.1305 |
6.7124 |
14.2736 |
44.0573 |
0.2735 |
| 007 |
11.4810 |
3.2152 |
10.9508 |
20.6005 |
0.2654 |
| 008 |
11.5334 |
3.2563 |
11.0081 |
20.6385 |
0.2318 |
| 009 |
12.5853 |
3.7899 |
11.9621 |
24.2876 |
0.3319 |
| 010 |
12.2648 |
3.6596 |
11.6532 |
22.9485 |
0.3181 |
| 011 |
11.3285 |
3.1671 |
10.8179 |
20.4061 |
0.3992 |
| 012 |
11.7637 |
3.4383 |
11.1925 |
21.9481 |
0.2730 |
| 013 |
8.8461 |
2.8193 |
8.3511 |
21.5170 |
0.2380 |
| 014 |
8.7551 |
2.8603 |
8.2447 |
21.2991 |
0.2855 |
| 015 |
10.9730 |
3.0474 |
10.4928 |
21.9112 |
0.2779 |
| 016 |
8.8780 |
1.9557 |
8.6431 |
22.0899 |
0.2633 |
| 017 |
10.2374 |
2.3822 |
9.9146 |
21.5578 |
0.1848 |
| 018 |
9.5924 |
2.4406 |
9.2279 |
21.7952 |
0.2006 |
| 019 |
8.4827 |
2.5767 |
8.0577 |
20.9860 |
0.3444 |
| 020 |
8.7680 |
2.7013 |
8.3145 |
21.3158 |
0.2217 |
| 021 |
8.8530 |
2.7921 |
8.3737 |
21.2475 |
0.2372 |
| 022 |
8.8320 |
2.7925 |
8.3514 |
21.3483 |
0.1548 |
| 023 |
10.8246 |
2.1211 |
10.5724 |
21.4396 |
0.1816 |
| 024 |
10.8400 |
2.7784 |
10.4344 |
21.5516 |
0.1654 |
| 025 |
7.6109 |
2.8939 |
7.0050 |
20.0794 |
0.3764 |
| 026 |
7.6934 |
2.6334 |
7.2030 |
19.6203 |
0.1774 |
| 027 |
7.5142 |
2.5460 |
7.0364 |
19.7528 |
0.3131 |
| 028 |
7.5469 |
3.2641 |
6.7746 |
20.8182 |
0.9315 |
| 029 |
7.4710 |
2.7430 |
6.9286 |
20.2663 |
0.7569 |
| 030 |
7.6381 |
2.5936 |
7.1571 |
20.3221 |
0.5538 |
| Averages |
10.1231 |
3.0166 |
9.5623 |
21.8932 |
0.3550 |
