Triangulate points from a calibrated fisheye camera pair

asked 2019-05-07 08:38:28 -0500

antithing gravatar image

I have a stereo camera pair, made up of fisheye lenses.

I have an application that successfully calibrates and rectifies the images. The rectification looks good. I match features on the two images, this also looks good.

Now, i need to triangulate the points, but i cannot seem to get a good result.

I have checked:

The undistortion on its own, using cv:fisheye::undistort. The rectification by displaying the two images with horizontal lines. The feature matches with cv::DrawMatches

All of these steps look great, so I am almost certain that the calibration matrices are valid, but the following triangulation step outputs garbage.

i have been struggling with this for a week, so any help or ideas would be very much appreciated!

What am i doing wrong?

My triangulation code is:

void testTri(Mat K1, Vec4d D1, Mat R1, Mat K2, Vec4d D2, Mat R2, Vec3d T, cv::Mat P1, Mat P2)
{

    cv::Ptr<cv::ORB> orb = ORB::create(5000);

    vector<KeyPoint> kp1, kp2;
    Mat d1, d2;

    Mat orig_left, orig_right;
    orig_left = imread("/imgs_for_test/7_left.png");
    orig_right = imread("/imgs_for_test/7_right.png");

    cv::Mat lmapx, lmapy, rmapx, rmapy;
    cv::fisheye::initUndistortRectifyMap(K1, D1, R1, K1, Size(w, h), CV_16SC2, lmapx, lmapy);

    cv::fisheye::initUndistortRectifyMap(K2, D2, R2, K1, Size(w, h), CV_16SC2, rmapx, rmapy);
    Mat undist_left, undist_right;
    cv::remap(orig_left, undist_left, lmapx, lmapy, cv::INTER_LINEAR);
    cv::remap(orig_right, undist_right, rmapx, rmapy, cv::INTER_LINEAR);

    orb->detectAndCompute(undist_left, Mat(), kp1, d1);
    orb->detectAndCompute(undist_right, Mat(), kp2, d2);

    std::vector<cv::DMatch> matches;

    matches = RobustMatching(d1, d2, kp1, kp2);

    if (matches.empty())
    {
        std::cout << "No row matches for triangulation found." << std::endl;

    }
    cv::Mat outMatched;
    std::vector<char> mask(matches.size(), 1);
    cv::drawMatches(undist_left, kp1, undist_right, kp2, matches, outMatched, cv::Scalar::all(-1), cv::Scalar::all(-1), mask);
    cv::imshow("matched", outMatched);
    cv::waitKey(1);

    std::vector<cv::Point2f> pntsMatchedLeft;
    std::vector<cv::Point2f> pntsMatchedRight;

    if (matches.size() > 5)
    {
        for (int x = 0; x < matches.size(); x++)
        {
            cv::Point2f pnt2dL = kp1[matches[x].queryIdx].pt;
            pntsMatchedLeft.push_back(pnt2dL);

            cv::Point2f pnt2dR = kp2[matches[x].trainIdx].pt;
            pntsMatchedRight.push_back(pnt2dR);


        }


        // Camera 1 Projection Matrix K[I|0]
        cv::Mat PP1(3, 4, cv::DataType<float>::type);
        K1.copyTo(PP1.rowRange(0, 3).colRange(0, 3));  //K1 is the camera matrix


    //stereo 
        float tx = -4.1392073003541432e-02; //Stereo baseline
        float ty = 0;
        float tz = 0;

        //rotation ( images are rectified, so this is zero)
        float rots[9] = { 1,0,0,0,1,0,0,0,1 };
        cv::Mat R0 = cv::Mat(3, 3, CV_32F, rots);


        //translation. (stereo camera, rectified images, baseline)
        float trans[3] = { tx,ty,tz };
        cv::Mat t = cv::Mat(3, 1, CV_32F, trans);


        // Camera 2 Projection Matrix K[R|t]
        cv::Mat PP2(3, 4, CV_32F);
        R0.copyTo(PP2.rowRange(0, 3).colRange(0, 3));
        t.copyTo(PP2.rowRange(0, 3).col(3));
        PP2 = K2 * PP2;


        Mat points3Dnorm;

        cv::Mat point3d_homo;
        cv::triangulatePoints(PP1, PP2,
            pntsMatchedLeft, pntsMatchedRight,
            point3d_homo);

        assert(point3d_homo.cols == triangulation_points1.size());


        point3d_homo.copyTo(points3Dnorm);

        std::ofstream fsD;
        std::string filenameD = "localMap.csv";
        fsD.open(filenameD);

        for ...
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