Rough Terrain Vehicle-Camera Calibration
Last updated
Last updated
Calibration list page, where the users can load an existing dataset or create a new one.
2. New calibration selection modal.
3. Get started page of vehicle-camera setup.
4. Calibration settings modal.
Dataset name can be added here.
The user has to select the shape of the vehicle. Either rectangle or trapezoid.
For rectangle-shaped vehicles, users can input the measured values
For trapezoid-shaped vehicles, users can input the following measured values.
Description for vehicle details:
Configure checkerboard and Aruco:
AruCo markers are used for automatic wheel detection. Add the measurement of the marker.
Similarly, checkerboard configurations need to be updated.
5. Enter the intrinsic parameters for the mounted camera.
Intrinsic parameters for the camera are to be added here. Users have three options.
Users can use the intrinsic calibration tool and calibrate the results. Save them to profile and then load them here.
Or users can also load the JSON file.
6. Add images related to the mounted camera. One for the left view and the other for the right view.
7. Detect the checkerboard corners for both the mounted camera images.
Click on detect corners to get the checkerboard corners auto-detected.
Or else manually add the border corners to get all checkerboard corners.
8. Enter the intrinsic parameters for the external camera.
Intrinsic parameters for the camera are to be added here. Users have three options.
Users can use the intrinsic calibration tool and calibrate the results. Save them to profile and then load them here.
Or users can also load the JSON file.
9. Upload images are taken from the external camera for the left view.
10. Detect the checkerboard corners for all the left view external camera images.
Click on detect corners to get the checkerboard corners auto-detected.
Or else manually add the border corners to get all checkerboard corners.
11. Front and rear wheels are auto-detected.
Wheel points are auto-detected, users can view the markers by selecting undistorted images.
12. Upload images taken from the external camera for the right view.
13. Detect the checkerboard corners for both the right view external camera images.
Click on detect corners to get the checkerboard corners auto-detected.
Or else manually add the border corners to get all checkerboard corners.
14. Front and rear wheels are auto-detected.
Wheel points are auto-detected, users can view the markers by selecting undistorted images.
15. Click on the run calibration button. This takes all the input configuration and the file data to get the calibrated results.
16. The Top right bar shows the extrinsic parameters.
17. Visualize button shows the 3d representation of the car and its wheels. Along with the camera center and its frustum.
18. Export option helps the user to export the calibrated data for the mounted camera with the vehicle.
19. Users can check the error stats and add more images to see the change in error stats.
Reprojection Error: Its value is the mean delta of the marked wheel point and the reprojection of the calibrated wheel position. It's measured in pixels.
Translation Error: Its value is the mean delta of the distance between the ray produced from marked wheel points and the calibrated wheel position in 3d space. It's measured in meters.
We have a save option in the top right corner. A user can click on the Save button to save the calibration dataset at any time during the calibration process.
roll, pitch, and yaw are in degrees and px, py, pz are in meters.
roll, pitch, and yaw, px, py, pz are the extrinsic parameters downloaded from the calibration tool.
vehiclePoint3D is the 3d coordinates of a point in the vehicle coordinate system.
imagePoint3D is the 3d coordinates of a point in the camera coordinate system.
