Transform via ArUco marker

This tutorial demonstrates how to estimate the pose of the ArUco marker and transform a point cloud using a 4x4 homogeneous transformation matrix to the ArUco marker coordinate system.

Tip

Zivid calibration board contains an ArUco marker.

First, we load a point cloud of an ArUco marker.

C++

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const auto arucoMarkerFile = std::string(ZIVID_SAMPLE_DATA_DIR) + "/ArucoMarkerInCameraOrigin.zdf";
std::cout << "Reading ZDF frame from file: " << arucoMarkerFile << std::endl;
const auto frame = Zivid::Frame(arucoMarkerFile);
auto pointCloud = frame.pointCloud();

We can open the original point cloud in Zivid Studio and inspect it.

Note

The original point cloud is also in Sample Data.

Now, we can manually set the Z Range from 825 mm to 950 mm in the Depth view. This allows us to see that the marker is at approximately 840 mm distance, and that there is an angle between the camera and the marker frame.

Since we are using OpenCV, we need to convert the 2D image to OpenCV image format.

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std::cout << "Converting to OpenCV image format" << std::endl;
const auto bgrImage = pointCloudToColorBGR(pointCloud);
const auto grayImage = colorBGRToGray(bgrImage);

Then we configure the ArUco marker.

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std::cout << "Configuring ArUco marker" << std::endl;
const auto markerDictionary = cv::aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_100);
std::vector<int> markerIds;
std::vector<std::vector<cv::Point2f>> markerCorners;
cv::Ptr<cv::aruco::DetectorParameters> detectorParameters = cv::aruco::DetectorParameters::create();
detectorParameters->cornerRefinementMethod = cv::aruco::CORNER_REFINE_SUBPIX;

We then detect the ArUco marker in the 2D image and display it.

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std::cout << "Detecting ArUco Marker" << std::endl;
cv::aruco::detectMarkers(grayImage, markerDictionary, markerCorners, markerIds, detectorParameters);

std::cout << "Displaying detected ArUco marker" << std::endl;
cv::aruco::drawDetectedMarkers(bgrImage, markerCorners);
displayBGR(bgrImage, "ArucoMarkerDetected");

Then we estimate the pose of the ArUco marker.

C++

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std::cout << "Estimating pose of detected ArUco marker" << std::endl;
const auto transformMarkerToCamera = estimateArUcoMarkerPose(pointCloud, markerCorners[0]);
const auto transformCameraToMarker = invertAffineTransformation(transformMarkerToCamera);

Lastly, we transform the point cloud to the ArUco marker coordinate system and save the transformed point cloud to disk.

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std::cout << "Transforming point cloud from camera frame to ArUco marker frame" << std::endl;
pointCloud.transform(transformCameraToMarker);
std::cout << "Saving transformed point cloud to file: " << arucoMarkerTransformedFile << std::endl;
frame.save(arucoMarkerTransformedFile);

Now we can open the transformed point cloud in Zivid Studio and inspect it.

Note

Zoom out in Zivid Studio to find the data because the viewpoint origin is inadequate for transformed point clouds.

We can now manually set the Z Range from -5 mm to 5 mm in the Depth view to filter out all data except the ArUco marker plate This allows us to see that the point cloud origin is in the marker frame.