From f6d9e179dc11ba7585136f778c4862f5b28168ab Mon Sep 17 00:00:00 2001 From: Fujun Ruan <85526629+Flowerst-0416@users.noreply.github.com> Date: Tue, 6 Dec 2022 03:12:22 -0500 Subject: [PATCH] readme image gif update --- README.md | 76 +++++++++++++++++++------------- images/Laser_Cali.png | Bin 52740 -> 273731 bytes images/handeye_evl.png | Bin 0 -> 102514 bytes images/handeye_result_shown.png | Bin 0 -> 79174 bytes images/test1.png | Bin 0 -> 201703 bytes 5 files changed, 45 insertions(+), 31 deletions(-) create mode 100644 images/handeye_evl.png create mode 100644 images/handeye_result_shown.png create mode 100644 images/test1.png diff --git a/README.md b/README.md index b1f1040..efc850a 100644 --- a/README.md +++ b/README.md @@ -98,6 +98,10 @@ cd ~/Calibration_ws source devel/setup.bash roslaunch multi_calibration auto_calibration.launch directory:="[file position]" camera_intrinsic:=1 ``` +The motion of the robot arm will be like: +

+ Image Viewer Panel +

#### 1.2.2 Calibration Process Go to the folder where images collected from last step are saved @@ -142,33 +146,20 @@ rosrun camera_model Calibration -w 7 -h 10 -s 5 -i ./ -v --camera-model PINHOLE Update the pinhole calibration result (fx fy cx cy) to `rectCameraMatrix` in `~/Calibration_ws/src/multi_calibration/cfg/calib_params.yaml` #### 1.2.3 Result Checking -To check the calibration result, run all the same four terminals with auto_calibration in camera_rect_intrinsic mode +To check the calibration result, run **auto_calibration.launch** again with **camera_intrinsic:=2** shown below. ```shell cd ~/Calibration_ws source devel/setup.bash -roslaunch multi_calibration auto_calibration.launch directory:="[file position]" camera_rect_intrinsic:=1 +roslaunch multi_calibration auto_calibration.launch directory:="[file position]" camera_intrinsic:=2 ``` - -In another terminal window open rqt. - -```shell -rqt -``` - -Go to Plugins -> Visualization -> Image Viewer +Then you can see the preset ```rqt``` window which shows both raw image and rect_image.

- Image Viewer + Image Viewer Panel

-In the Image Viewer panel switch to the `blaser_cam/image_rect_node` topic, this would display the undistorted image. All the straight lines in real world should appear as perfectly straight on the image. - -

- Image Viewer Panel -

- ## 2. Hand-eye Calibration ![Flowchart](images/Hand_eye_calibration.png) @@ -176,7 +167,7 @@ In the Image Viewer panel switch to the `blaser_cam/image_rect_node` topic, this * UR robot arm * Camera holder([Example](https://drive.google.com/file/d/1i7l1ikb1o2ocoi0iMsCJhSHDmBsIzraE/view?usp=sharing)) * April tag holder([Example](https://drive.google.com/file/d/12sZWZGFeq9ehgpnKUAZS80BMfkZedMsY/view?usp=sharing)) -* [Hand eye calibration ipynb](Hand_eye.ipynb) +* [Hand eye calibration ipynb](Hand_eye.ipynb) (no need after update) ### 2.2 Calibration process #### 2.2.1 Tag Size Measurement @@ -188,7 +179,11 @@ Note: The tag size should not be measured from the outside of the tag. The tag s Tag size example

-#### 2.2.2 Tag Measurement + + + +#### 2.2.2 Calibration Process + - Getting the *camera to Tag* transform: - Start the ximea camera node @@ -205,22 +200,33 @@ Note: The tag size should not be measured from the outside of the tag. The tag s cd ~/Calibration_ws source devel/setup.bash roslaunch multi_calibration auto_calibration.launch hand_eye:=1 - ``` -Now the **camera to Tag** transform can be viewed by echoing the **tf** rostopic. -- Getting the *end-effector to Tag* transform: Use the CAD of your April tag holder and Camera holder to obtain the transform. -#### 2.2.3 Calibration Process After properly setting up the Apriltag pose estimation pipeline(that includes image undistortion and publishing updated rectified camera matrix), the **camera to Tag** transform should be available to you. With the measurement of **camera to Tag** and **end-effector to Tag**, input them into -[Jupyter Notebook for Hand-eye calibration](https://drive.google.com/file/d/1x8It3NmqM_Qm07OM-dieFRudrayFTtfS/view?usp=sharing) and get the **EE to Camera** transform matrix. +[Calibration yaml file](https://github.com/Flowerst-0416/UR_arm_camera_calibration/blob/main/multi_calibration/cfg/handeye_params.yaml) and get the **EE to Camera** transform matrix which will output to [Handeye result colletion file](https://github.com/Flowerst-0416/UR_arm_camera_calibration/blob/main/multi_calibration/cfg/handeye_result.yaml) + +#### 2.2.3 Result Checking + +After launch the ```auto_calibration.launch``` in handeye mode, an rviz will show out to help you evaluate the tag detection and measurement. + +

+ rviz window +

+ +The calculattion result will also shown in the matplot which you can use it to compare with the predict value and do futher evaluation. + +

+ rviz window +

-#### 2.2.4 Result Checking As a sanity check for the calculation, the tag position derived from your **end-effector to Tag** transform and from **end-effector-camera-tag** transform chains should be exactly the same. ## 3. Camera-laser extrinsics -![Flowchart](images/Laser_Cali.png) +

+ Flowchart +

### 3.1 Hardware needed * UR5e robot arm * Camera holder([Example](https://drive.google.com/file/d/12sZWZGFeq9ehgpnKUAZS80BMfkZedMsY/view?usp=sharing)) @@ -256,7 +262,7 @@ give example config and dir), which will be used in the calibration later. To test these parameters, run `laser_stripe_detector` node with sample images and the config file. (**todo turn on lsd visualization**). -In order to collect more image for laser calibration, please go to **cali_ws** and run the following code: +In order to collect more image for laser calibration, please go to **Calibration_ws** and run the following code: Open four terminal: For the First one, connect the arm: @@ -283,10 +289,18 @@ Next enable the auto_calibration in the laser_cam mode: ```shell cd ~/Calibration_ws source devel/setup.bash -roslaunch multi_calibration auto_calibration.launch directory:="[file position]" laser_cam:=1 +roslaunch multi_calibration auto_calibration.launch laser_cam:=1 ``` +The motion of the robot arm will be like: +

+ Image Viewer Panel +

+After collecting the image relaunch the ```auto_calibration.launch``` with laser_cam:=2. The filter program will automatically start detecting if there is any noise image. Then it will run the laser calibration program and output the result. + +**Important:** please paste the result of the intrinsics calibration into [laser_calib.yaml](https://github.com/Flowerst-0416/UR_arm_camera_calibration/blob/main/multi_calibration/cfg/laser_calib.yaml) ```shell -cd ~/Calibration_ws/laser_calib -rosrun multi_calibration laser_calib -i ./ -c ./laser_calib.yaml -``` \ No newline at end of file +cd ~/Calibration_ws +source devel/setup.bash +roslaunch multi_calibration auto_calibration.launch laser_cam:=2 +``` diff --git a/images/Laser_Cali.png b/images/Laser_Cali.png index 839aa7b7295cf90bb9de52a338715b7bfc8de6f9..c66f830a0206d184c898b48e532a3ea584888e31 100644 GIT binary patch literal 273731 zcmeFZcT`jB_a+|fD1v~6j-r5y0#c-d1qGz3Nbe#+q)P87sDKCvD7{z^lwPD0iYTF@ zAiV^nNeL~q07+&a^nSi~X3eZMYt3(de@*VXOHFc4&UxSc?)~iNdEOIrQ%&LEewO_x z6zZUoqWoSYuvFYLgU>2CPN$ru#sn0-RoEot~*&s}8&c@zct?+qp`65gSKr6*mI|5 zspDUr&M>h#AFOXfFK(kArZv{ZVHMojmt=gwqj;J=v@eNeCA+ZbUA_0RmbDE2)9#;r 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