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--- tutorials:camera_arm_calibration [2019/11/08 17:21] – dgarcia
+++ tutorials:camera_arm_calibration [2022/09/20 00:08] (current) – external edit 127.0.0.1
@@ Line 43: / Line 43: @@
   * Move the generated file to the following directory:
-  mv <filename>.csv  <catkin_workspace>/src/oms-cylinder/camera2png/data
+  mv <poses-filename>.csv  <catkin_workspace>/src/oms-cylinder/camera2png/data
 ==== Taking Photos ====
@@ Line 53: / Line 53: @@
     - Enable auto white Balance
     - Auto Exposure Priority
+    - Set White Balance to: 3760
   * Close ''realsense-viewer''
+  * Remember to source tour workspace before executing the following commands.
   * Now we need to launch the camera node:
@@ Line 64: / Line 66: @@
   mkdri outpout
   cd <catkin_workspace>/src/oms-cylinder/camera2png/scripts/
-  python camera2png.py ../data/example.py ../data/poses.csv ../data/output
+  python camera2png.py ../data/example.py ../data/<poses-file-name>.csv ../data/output
+=== Trouble shooting ===
+Sometimes the KUKA robot stops working (we are not sure why). When that happens, you will have to:
+  * Kill the ''camera2png'' script
+  * Re-enable the arm
+  * check in ''<catkin_workspace>/src/oms-cylinder/camera2png/data'' which was the last generated photo. Lets say it was ''N''.
+  * Restart the ''camera2png'' script like this:
+  python camera2png.py ../data/example.py ../data/<poses-file-name>.csv ../data/output --start <N+1>
 ==== Executing the calibration script ====
+  * First we have to measure the transformations between the kinematic arm base to the camera and from the wrist to the calibration board. This will be used as the initialization guess for the calibration program to use. The guess has to be given in translation ''vector(x, y, z)'' and orientation ''quaternion(x, y, z, w)''. This configuration has to be stored in YAML format in the following file: ''<catkin_ws>/src/oms-cylinder/oms_launcher/cal/configs/static_camera_guesses.yml''. Here you can find an example (Do **NOT** use this as your guess).
+<code yaml>
+base_to_camera_guess:
+  x: 1.04
+  y: 1.1
+  z: 0.0
+  qx: 0.3
+  qy: 0.4
+  qz: 0.5
+  qw: 0.01
+wrist_to_target_guess:
+  x: 0.109
+  y: 0.2
+  z: -0.05
+  qx: -0.-4
+  qy: 0.5
+  qz: 0.5
+  qw: -0.4
+</code>
+  * You also need to get the camera intrinsics and store them in another YAML. Here you can find and example:
+<code yaml>
+intrinsics:
+  fx: 1387.6160888671875
+  fy: 1387.5479736328125
+  cx: 943.9945678710938
+  cy: 561.1880493164062
+</code>
+  * Then you need to provide a configuration file for the calibration board, like so:
+<code yaml>
+target_definition:
+  rows: 10
+  cols: 10
+  spacing: 0.01861
+</code>
+  * Now we have to create a separate directory for images and poses.
+<code bash>
+mv <catkin_workspace>/src/oms-cylinder/camera2png/data <catkin_ws>/src/oms-cylinder/oms_launcher/cal/configs
+cd <catkin_ws>/src/oms-cylinder/oms_launcher/cal/configs/data
+mkdir images
+mkdir poses
+mv *.yml poses
+mv *.png images
+</code>
+  * Now we have to crate a YAML listing all the files that have to be taken into consideration by the calibration script, in the ''configs/data'' directory.
+<code yaml>
+# TODO: Fix this exxample
+- pose: "000000.yml",
+  image: "000000.png"
+- pose: "000001.yml",
+  image: "000001.png"
+  .
+  .
+  .
+</code>
+  * Launch the static calibration script.
+<code bash>
+roslaunch oms-cylinder/oms_launcher/launch/static_calibration.launch
+</code>
+  * This will start the calibration script. This will open a window that will show you the photos that were taken, you should see some color dots. If your data set is small, you will have to press ''ENTER'' for each image. If your data set is big, only one ''ENTER'' is required (we have no idea what is the threshold, but is seems to be around 2000 poses).
+  * You must wait when you press ''ENTER'' and the program doesn't respond, there are some images that take longer to analyze than others. When you reach the last image, the //iamge-window-interface// will gt stuck, Do Not press ''ENTER''. Wait until the algorithm converges.
+  * Once the algorithm converges you will see a lot of results like the one shown below. The calibration result is the first one.
+<code>
+Did converge?: 1
+Initial cost?: 47853.2 (pixels per dot)
+Final cost?: 0.655122 (pixels per dot)
+BASE TO CAMERA:
+ -0.208397  -0.664739   0.717421 -0.0773392
+ -0.967382  0.0320803  -0.251281   0.220998
+.144021  -0.746387  -0.649742    1.43606
+          0          0          1
+--- URDF Format Base to Camera ---
+xyz="-0.0773392 0.220998 1.43606"
+rpy="0.854511(48.9599 deg) -2.99707(-171.719 deg) 1.35862(77.8429 deg)"
+qxyzw="-0.593562 0.687426 -0.362827 0.208531"
+BASE TO CAMERA:
+ -0.0184899    0.999697  -0.0162778  -0.0817589
+-0.00618554   0.0161659     0.99985    0.139854
+.99981   0.0185878  0.00588475 -0.00619646
+           0           0           1
+--- URDF Format Base to Camera ---
+xyz="-0.0817589 0.139854 -0.00619646"
+rpy="-1.87741(-107.567 deg) -1.59029(-91.1172 deg) 0.322833(18.497 deg)"
+qxyzw="-0.48976 -0.507142 -0.502048 0.500889"
+</code>
 ==== Verify that the calibration is correct ====