Fix apps/calibration location

doc/tutorial/calibration/tutorial-calibration-extrinsic.dox

@@ -31,10 +31,10 @@ The calibration process described in this tutorial consists in 3 steps:
    to estimate the \f$^e{\bf M}_c\f$ transformation.
 
 Note that all the material (source code) described in this tutorial is part of ViSP source code
-(in `tutorial/calibration` folder) and could be downloaded using the following command:
+(in `apps/calibration` folder) and could be downloaded using the following command:
 
 \verbatim
-$ svn export https://github.com/lagadic/visp.git/trunk/tutorial/calibration
+$ svn export https://github.com/lagadic/visp.git/trunk/apps/calibration
 \endverbatim
 
 \section calib_ext_recommendation Recommendations
@@ -102,7 +102,7 @@ If you want the parameters with distortion, you need to achieve a calibration as
 
 As an example, in ViSP source code you will find a dataset corresponding to data acquired with a real robot:
 \verbatim
-$ cd $VISP_WS/visp-build/tutorial/calibration
+$ cd $VISP_WS/visp-build/apps/calibration
 $ ls *.{xml,png,yaml}
 camera.xml   image-3.png  image-6.png  pose_fPe_1.yaml  pose_fPe_4.yaml  pose_fPe_7.yaml
 image-1.png  image-4.png  image-7.png  pose_fPe_2.yaml  pose_fPe_5.yaml  pose_fPe_8.yaml
@@ -286,10 +286,10 @@ vpRealSense2 class usage.
 <b>Step 1: Acquire robot poses and images</b>
 
 Connect the Realsense D435 camera to the computer, put the chessboard in the camera field of view, enter in
-`tutorial/calibration` folder and run `visp-acquire-franka-calib-data` binary to acquire the images and the
+`apps/calibration` folder and run `visp-acquire-franka-calib-data` binary to acquire the images and the
 corresponding robot end-effector positions:
 
-    $ cd tutorial/calibration
+    $ cd apps/calibration
     $ ./visp-acquire-franka-calib-data
 
 By default the robot controller IP is `192.168.1.1`. If your Franka has an other IP (let say 10.0.0.2) use
@@ -392,11 +392,11 @@ vpRealSense2 class usage.
 <b>Step 1: Acquire robot poses and images</b>
 
 Connect the Realsense camera to the computer, put the chessboard in the camera field of view, enter in
-`tutorial/calibration` folder and run `visp-acquire-universal-robots-calib-data` binary to acquire the images and
+`apps/calibration` folder and run `visp-acquire-universal-robots-calib-data` binary to acquire the images and
 the corresponding robot end-effector positions:
 
 \verbatim
-$ cd tutorial/calibration
+$ cd apps/calibration
 $ ./visp-acquire-universal-robots-calib-data
 \endverbatim
 

doc/tutorial/visual-servo/tutorial-franka-ibvs.dox

@@ -16,7 +16,7 @@ An example of image-based visual servoing using Panda robot equipped with a Real
 
 - Attach your Realsense camera to the robot end-effector
 - Put an Apriltag in the camera field of view
-- If not already done, follow \ref tutorial-calibration-extrinsic to estimate \f$^e{\bf M}_c\f$ the homogeneous transformation between robot end-effector and camera frame. We suppose here that the file is located in `tutorial/calibration/eMc.yaml`.
+- If not already done, follow \ref tutorial-calibration-extrinsic to estimate \f$^e{\bf M}_c\f$ the homogeneous transformation between robot end-effector and camera frame. We suppose here that the file is located in `apps/calibration/eMc.yaml`.
 
 Now enter in `example/servo-franka folder` and run `servoFrankaIBVS` binary using `--eMc` to locate the file containing the \f$^e{\bf M}_c\f$ transformation. Other options are available. Using `--help` show them:
 
@@ -26,7 +26,7 @@ Now enter in `example/servo-franka folder` and run `servoFrankaIBVS` binary usin
 
 Run the binary activating the plot and using a constant gain:
 
-    $ ./servoFrankaIBVS --eMc ../../tutorial/calibration/eMc.yaml --plot
+    $ ./servoFrankaIBVS --eMc ../../apps/calibration/eMc.yaml --plot
 
 Use the left mouse click to enable the robot controller, and the right click to quit the binary.
 
@@ -40,15 +40,15 @@ At this point the behaviour that you should observe is the following:
 
 You can also activate an adaptive gain that will make the convergence faster:
 
-    $ ./servoFrankaIBVS --eMc ../../tutorial/calibration/eMc.yaml --plot --adaptive_gain
+    $ ./servoFrankaIBVS --eMc ../../apps/calibration/eMc.yaml --plot --adaptive_gain
 
 You can also start the robot with a zero velocity at the beginning introducing task sequencing option:
 
-    $ ./servoFrankaIBVS --eMc ../../tutorial/calibration/eMc.yaml --plot --task_sequencing
+    $ ./servoFrankaIBVS --eMc ../../apps/calibration/eMc.yaml --plot --task_sequencing
 
 And finally you can activate the adaptive gain and task sequencing:
 
-    $ ./servoFrankaIBVS --eMc ../../tutorial/calibration/eMc.yaml --plot --adaptive_gain --task_sequencing
+    $ ./servoFrankaIBVS --eMc ../../apps/calibration/eMc.yaml --plot --adaptive_gain --task_sequencing
 
 To learn more about adaptive gain and task sequencing see \ref tutorial-boost-vs.
 
@@ -61,6 +61,6 @@ from a real Franka robot, like in the next video, we recommend to make a tour on
 <p align="center"><iframe width="560" height="315" src="https://www.youtube.com/embed/02Bx093Fuak" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></p>
 \endhtmlonly
 
-You can also follow \ref tutorial-ibvs that will give some hints on image-based visual servoing in simulation with a free flying camera. 
+You can also follow \ref tutorial-ibvs that will give some hints on image-based visual servoing in simulation with a free flying camera.
 
 */

doc/tutorial/visual-servo/tutorial-franka-pbvs.dox

@@ -258,7 +258,7 @@ An example of position-based visual servoing using Panda robot equipped with a R
 
 - Attach your Realsense camera to the robot end-effector
 - Put an Apriltag in the camera field of view
-- If not already done, follow \ref tutorial-calibration-extrinsic to estimate \f$^e{\bf M}_c\f$ the homogeneous transformation between robot end-effector and camera frame. We suppose here that the file is located in `tutorial/calibration/eMc.yaml`.
+- If not already done, follow \ref tutorial-calibration-extrinsic to estimate \f$^e{\bf M}_c\f$ the homogeneous transformation between robot end-effector and camera frame. We suppose here that the file is located in `apps/calibration/eMc.yaml`.
 
 Now enter in `example/servo-franka folder` and run `servoFrankaPBVS` binary using `--eMc` to locate the file containing the \f$^e{\bf M}_c\f$ transformation. Other options are available. Using `--help` show them:
 
@@ -268,7 +268,7 @@ Now enter in `example/servo-franka folder` and run `servoFrankaPBVS` binary usin
 
 Run the binary activating the plot and using a constant gain:
 
-    $ ./servoFrankaPBVS --eMc ../../tutorial/calibration/eMc.yaml --plot
+    $ ./servoFrankaPBVS --eMc ../../apps/calibration/eMc.yaml --plot
 
 \note If you encounter the following error message:
 \verbatim
@@ -287,15 +287,15 @@ Now you should see new window that shows the image from the camera like in the n
 
 You can also activate an adaptive gain that will make the convergence faster:
 
-    $ ./servoFrankaPBVS --eMc ../../tutorial/calibration/eMc.yaml --plot --adaptive_gain
+    $ ./servoFrankaPBVS --eMc ../../apps/calibration/eMc.yaml --plot --adaptive_gain
 
 You can also start the robot with a zero velocity at the beginning introducing task sequencing option:
 
-    $ ./servoFrankaPBVS --eMc ../../tutorial/calibration/eMc.yaml --plot --task_sequencing
+    $ ./servoFrankaPBVS --eMc ../../apps/calibration/eMc.yaml --plot --task_sequencing
 
 And finally you can activate the adaptive gain and task sequencing:
 
-    $ ./servoFrankaPBVS --eMc ../../tutorial/calibration/eMc.yaml --plot --adaptive_gain --task_sequencing
+    $ ./servoFrankaPBVS --eMc ../../apps/calibration/eMc.yaml --plot --adaptive_gain --task_sequencing
 
 To learn more about adaptive gain and task sequencing see \ref tutorial-boost-vs.
 

doc/tutorial/visual-servo/tutorial-universal-robot-ibvs.dox

@@ -128,7 +128,7 @@ An example of image-based visual servoing using a robot from Universal Robots eq
 
 - Attach your Realsense camera to the robot end-effector. To this end, we provide a CAD model of a support that could be 3D printed. The FreeCAD model is available [here](https://github.com/lagadic/visp/tree/master/example/servo-universal-robots).
 - Put an Apriltag in the camera field of view
-- If not already done, follow \ref tutorial-calibration-extrinsic to estimate \f$^e{\bf M}_c\f$, the homogeneous transformation between robot end-effector and camera frame. We suppose here that the file is located in `tutorial/calibration/ur_eMc.yaml`.
+- If not already done, follow \ref tutorial-calibration-extrinsic to estimate \f$^e{\bf M}_c\f$, the homogeneous transformation between robot end-effector and camera frame. We suppose here that the file is located in `apps/calibration/ur_eMc.yaml`.
 
 Now enter in `example/servo-universal-robots folder` and run `servoUniversalRobotsIBVS` binary using `--eMc` to locate the file containing the \f$^e{\bf M}_c\f$ transformation. Other options are available. Using `--help` show them:
 
@@ -144,7 +144,7 @@ $ ./servoUniversalRobotsIBVS --help
 Run the binary activating the plot and using a constant gain:
 
 \verbatim
-$ ./servoUniversalRobotsIBVS --eMc ../../tutorial/calibration/ur_eMc.yaml --plot
+$ ./servoUniversalRobotsIBVS --eMc ../../apps/calibration/ur_eMc.yaml --plot
 \endverbatim
 
 Use the left mouse click to enable the robot controller, and the right click to quit the binary.
@@ -160,19 +160,19 @@ At this point the behaviour that you should observe is the following:
 You can also activate an adaptive gain that will make the convergence faster:
 
 \verbatim
-$ ./servoUniversalRobotsIBVS --eMc ../../tutorial/calibration/ur_eMc.yaml --plot --adaptive_gain
+$ ./servoUniversalRobotsIBVS --eMc ../../apps/calibration/ur_eMc.yaml --plot --adaptive_gain
 \endverbatim
 
 You can also start the robot with a zero velocity at the beginning introducing task sequencing option:
 
 \verbatim
-$ ./servoUniversalRobotsIBVS --eMc ../../tutorial/calibration/ur_eMc.yaml --plot --task_sequencing
+$ ./servoUniversalRobotsIBVS --eMc ../../apps/calibration/ur_eMc.yaml --plot --task_sequencing
 \endverbatim
 
 And finally you can activate the adaptive gain and task sequencing:
 
 \verbatim
-$ ./servoUniversalRobotsIBVS --eMc ../../tutorial/calibration/ur_eMc.yaml --plot --adaptive_gain --task_sequencing
+$ ./servoUniversalRobotsIBVS --eMc ../../apps/calibration/ur_eMc.yaml --plot --adaptive_gain --task_sequencing
 \endverbatim
 
 \section ur_ibvs_next Next tutorial

doc/tutorial/visual-servo/tutorial-universal-robot-pbvs.dox

@@ -11,7 +11,7 @@ An example of position-based visual servoing using a robot from Universal Robots
 
 - Attach your Realsense camera to the robot end-effector. To this end, we provide a CAD model of a support that could be 3D printed. The FreeCAD model is available [here](https://github.com/lagadic/visp/tree/master/example/servo-universal-robots).
 - Put an Apriltag in the camera field of view
-- If not already done, follow \ref tutorial-calibration-extrinsic to estimate \f$^e{\bf M}_c\f$, the homogeneous transformation between robot end-effector and camera frame. We suppose here that the file is located in `tutorial/calibration/eMc.yaml`.
+- If not already done, follow \ref tutorial-calibration-extrinsic to estimate \f$^e{\bf M}_c\f$, the homogeneous transformation between robot end-effector and camera frame. We suppose here that the file is located in `apps/calibration/eMc.yaml`.
 
 Now enter in `example/servo-universal-robots folder` and run `servoUniversalRobotsPBVS` binary using `--eMc` to locate the file containing the \f$^e{\bf M}_c\f$ transformation. Other options are available. Using `--help` show them:
 
@@ -27,7 +27,7 @@ $ ./servoUniversalRobotsPBVS --help
 Run the binary activating the plot and using a constant gain:
 
 \verbatim
-$ ./servoUniversalRobotsPBVS --eMc ../../tutorial/calibration/ur_eMc.yaml --plot
+$ ./servoUniversalRobotsPBVS --eMc ../../apps/calibration/ur_eMc.yaml --plot
 \endverbatim
 
 Use the left mouse click to enable the robot controller, and the right click to quit the binary.
@@ -43,19 +43,19 @@ At this point the behaviour that you should observe is the following:
 You can also activate an adaptive gain that will make the convergence faster:
 
 \verbatim
-$ ./servoUniversalRobotsPBVS --eMc ../../tutorial/calibration/ur_eMc.yaml --plot --adaptive_gain
+$ ./servoUniversalRobotsPBVS --eMc ../../apps/calibration/ur_eMc.yaml --plot --adaptive_gain
 \endverbatim
 
 You can also start the robot with a zero velocity at the beginning introducing task sequencing option:
 
 \verbatim
-$ ./servoUniversalRobotsPBVS --eMc ../../tutorial/calibration/ur_eMc.yaml --plot --task_sequencing
+$ ./servoUniversalRobotsPBVS --eMc ../../apps/calibration/ur_eMc.yaml --plot --task_sequencing
 \endverbatim
 
 And finally you can activate the adaptive gain and task sequencing:
 
 \verbatim
-$ ./servoUniversalRobotsPBVS --eMc ../../tutorial/calibration/ur_eMc.yaml --plot --adaptive_gain --task_sequencing
+$ ./servoUniversalRobotsPBVS --eMc ../../apps/calibration/ur_eMc.yaml --plot --adaptive_gain --task_sequencing
 \endverbatim
 
 \section ur_pbvs_next Next tutorial