使用cv :: rgbd :: Odometry :: compute

Question

我正在使用C++和OpenCV与ROS的组合.我使用来自相机的实时图像(intel realsense R200).我从相机中获取深度和RGB图像.在我的c ++代码中,我想使用这些图像来获取odometry数据并从中获取轨迹.

我试图使用"cv :: rgbd :: Odometry :: compute"函数进行测距,但我总是得到假,因为返回值(代码中的"isSuccess"值始终为0).但我不知道哪个部分我做错了.

我使用ROS从相机读取我的图像,然后在回调函数中,首先我将所有图像转换为灰度,然后使用Surf功能检测功能.然后我想使用"compute"来获得当前帧和前一帧之间的转换.

据我所知,"Rt"和"inintRt"是函数的输出,所以它足以用正确的大小来构造它们.

有谁能看到这个问题？我错过了什么吗？

boost::shared_ptr<rgbd::Odometry> odom;

Mat Rt = Mat(4,4, CV_64FC1);
Mat initRt = Mat(4,4, CV_64FC1);

Mat prevFtrM; //mask Matrix of previous image
Mat currFtrM; //mask Matrix of current image
Mat tempFtrM;

Mat imgprev;// previous depth image
Mat imgcurr;// current depth image

Mat imgprevC;// previous colored image
Mat imgcurrC;// current colored image


void Surf(Mat img) // detect features of the img and fill currFtrM
{
    int minHessian = 400;
    Ptr<SURF> detector = SURF::create( minHessian );
    vector<KeyPoint> keypoints_1;

    currFtrM = Mat::zeros(img.size(), CV_8U);  // type of mask is CV_8U
    Mat roi(currFtrM, cv::Rect(0,0,img.size().width,img.size().height));
    roi = Scalar(255, 255, 255);

    detector->detect( img, keypoints_1, currFtrM );

    Mat img_keypoints_1;
    drawKeypoints( img, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
    //-- Show detected (drawn) keypoints
    imshow("Keypoints 1", img_keypoints_1 );
}


void Callback(const sensor_msgs::ImageConstPtr& clr, const sensor_msgs::ImageConstPtr& dpt)
{

    if(!imgcurr.data || !imgcurrC.data) // first frame
    {
        // depth image
        imgcurr = cv_bridge::toCvShare(dpt, sensor_msgs::image_encodings::TYPE_32FC1)->image;

        // colored image
        imgcurrC = cv_bridge::toCvShare(clr, "bgr8")->image;
        cvtColor(imgcurrC, imgcurrC, COLOR_BGR2GRAY);

        //find features in the image
        Surf(imgcurrC);
        prevFtrM = currFtrM;

        //scale color image to size of depth image
        resize(imgcurrC,imgcurrC, imgcurr.size());

        return;
    }

    odom = boost::make_shared<rgbd::RgbdOdometry>(imgcurrC, Odometry::DEFAULT_MIN_DEPTH(), Odometry::DEFAULT_MAX_DEPTH(),               Odometry::DEFAULT_MAX_DEPTH_DIFF(), std::vector< int >(), std::vector< float >(),               Odometry::DEFAULT_MAX_POINTS_PART(), Odometry::RIGID_BODY_MOTION);



    // depth image
    imgprev = imgcurr;
    imgcurr = cv_bridge::toCvShare(dpt, sensor_msgs::image_encodings::TYPE_32FC1)->image;

    // colored image
    imgprevC = imgcurrC;
    imgcurrC = cv_bridge::toCvShare(clr, "bgr8")->image;
    cvtColor(imgcurrC, imgcurrC, COLOR_BGR2GRAY);

    //scale color image to size of depth image
    resize(imgcurrC,imgcurrC, imgcurr.size());
    cv::imshow("Color resized", imgcurrC);

    tempFtrM = currFtrM;
    //detect new features in imgcurrC and save in a vector<Point2f>
    Surf( imgcurrC);

    prevFtrM = tempFtrM;

    //set camera matrix to identity matrix
    float vals[] = {619.137635, 0., 304.793791, 0., 625.407449, 223.984030, 0., 0., 1.};

    const Mat cameraMatrix = Mat(3, 3, CV_32FC1, vals);
    odom->setCameraMatrix(cameraMatrix);


    bool isSuccess = odom->compute( imgprevC, imgprev, prevFtrM,  imgcurrC, imgcurr, currFtrM, Rt, initRt );

    if(isSuccess)
        cout << "isSuccess   " << isSuccess << endl;

}

更新:我校准了相机并用实际值替换了相机矩阵.

Answer 1

有点晚了，但对某人来说仍然有用。

在我看来，您在计算中缺少外部校准：在我的实验中，R200 在 RGB 和深度相机之间有一个您没有考虑到的平移分量。此外，查看相机参数，深度和 RGB 具有不同的内在特性，并且颜色框架具有 MODIFIED_BROWN_CONRADY 镜头失真（但这很小），您是否对其进行了不失真处理？

显然，如果您已经完成所有这些步骤并将注册的 RGB 和深度保存到文件中，我可能是错的。