util3d_motion_estimation.cpp

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/*
Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
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#include "rtabmap/core/util3d_motion_estimation.h"

#include "rtabmap/utilite/UStl.h"
#include "rtabmap/utilite/UMath.h"
#include "rtabmap/utilite/ULogger.h"
#include "rtabmap/core/util3d_transforms.h"
#include "rtabmap/core/util3d_registration.h"
#include "rtabmap/core/util3d_correspondences.h"
#include "rtabmap/core/util3d.h"

#include "rtabmap/core/OptimizerG2O.h"

#if CV_MAJOR_VERSION < 3
#include "opencv/solvepnp.h"
#endif

namespace rtabmap
{

namespace util3d
{

Transform estimateMotion3DTo2D(
                        const std::map<int, cv::Point3f> & words3A,
                        const std::map<int, cv::KeyPoint> & words2B,
                        const CameraModel & cameraModel,
                        int minInliers,
                        int iterations,
                        double reprojError,
                        int flagsPnP,
                        int refineIterations,
                        const Transform & guess,
                        const std::map<int, cv::Point3f> & words3B,
                        double * varianceOut,
                        std::vector<int> * matchesOut,
                        std::vector<int> * inliersOut)
{
        UASSERT(cameraModel.isValidForProjection());
        UASSERT(!guess.isNull());
        Transform transform;
        std::vector<int> matches, inliers;

        if(varianceOut)
        {
                *varianceOut = 1.0;
        }

        // find correspondences
        std::vector<int> ids = uKeys(words2B);
        std::vector<cv::Point3f> objectPoints(ids.size());
        std::vector<cv::Point2f> imagePoints(ids.size());
        int oi=0;
        matches.resize(ids.size());
        for(unsigned int i=0; i<ids.size(); ++i)
        {
                std::map<int, cv::Point3f>::const_iterator iter=words3A.find(ids[i]);
                if(iter != words3A.end() && util3d::isFinite(iter->second))
                {
                        const cv::Point3f & pt = iter->second;
                        objectPoints[oi].x = pt.x;
                        objectPoints[oi].y = pt.y;
                        objectPoints[oi].z = pt.z;
                        imagePoints[oi] = words2B.find(ids[i])->second.pt;
                        matches[oi++] = ids[i];
                }
        }

        objectPoints.resize(oi);
        imagePoints.resize(oi);
        matches.resize(oi);

        UDEBUG("words3A=%d words2B=%d matches=%d words3B=%d",
                        (int)words3A.size(), (int)words2B.size(), (int)matches.size(), (int)words3B.size());

        if((int)matches.size() >= minInliers)
        {
                //PnPRansac
                cv::Mat K = cameraModel.K();
                cv::Mat D = cameraModel.D();
                Transform guessCameraFrame = (guess * cameraModel.localTransform()).inverse();
                cv::Mat R = (cv::Mat_<double>(3,3) <<
                                (double)guessCameraFrame.r11(), (double)guessCameraFrame.r12(), (double)guessCameraFrame.r13(),
                                (double)guessCameraFrame.r21(), (double)guessCameraFrame.r22(), (double)guessCameraFrame.r23(),
                                (double)guessCameraFrame.r31(), (double)guessCameraFrame.r32(), (double)guessCameraFrame.r33());

                cv::Mat rvec(1,3, CV_64FC1);
                cv::Rodrigues(R, rvec);
                cv::Mat tvec = (cv::Mat_<double>(1,3) <<
                                (double)guessCameraFrame.x(), (double)guessCameraFrame.y(), (double)guessCameraFrame.z());

                util3d::solvePnPRansac(
                                objectPoints,
                                imagePoints,
                                K,
                                D,
                                rvec,
                                tvec,
                                true,
                                iterations,
                                reprojError,
                                minInliers, // min inliers
                                inliers,
                                flagsPnP,
                                refineIterations);

                if((int)inliers.size() >= minInliers)
                {
                        cv::Rodrigues(rvec, R);
                        Transform pnp(R.at<double>(0,0), R.at<double>(0,1), R.at<double>(0,2), tvec.at<double>(0),
                                                   R.at<double>(1,0), R.at<double>(1,1), R.at<double>(1,2), tvec.at<double>(1),
                                                   R.at<double>(2,0), R.at<double>(2,1), R.at<double>(2,2), tvec.at<double>(2));

                        transform = (cameraModel.localTransform() * pnp).inverse();

                        // compute variance (like in PCL computeVariance() method of sac_model.h)
                        if(varianceOut && words3B.size())
                        {
                                std::vector<float> errorSqrdDists(inliers.size());
                                oi = 0;
                                for(unsigned int i=0; i<inliers.size(); ++i)
                                {
                                        std::map<int, cv::Point3f>::const_iterator iter = words3B.find(matches[inliers[i]]);
                                        if(iter != words3B.end() && util3d::isFinite(iter->second))
                                        {
                                                const cv::Point3f & objPt = objectPoints[inliers[i]];
                                                cv::Point3f newPt = util3d::transformPoint(iter->second, transform);
                                                errorSqrdDists[oi] = uNormSquared(objPt.x-newPt.x, objPt.y-newPt.y, objPt.z-newPt.z);
                                                //ignore very very far features (stereo)
                                                if(errorSqrdDists[oi] < 100.0f)
                                                {
                                                        ++oi;
                                                }
                                        }
                                }
                                errorSqrdDists.resize(oi);
                                if(errorSqrdDists.size())
                                {
                                        std::sort(errorSqrdDists.begin(), errorSqrdDists.end());
                                        double median_error_sqr = (double)errorSqrdDists[errorSqrdDists.size () >> 1];
                                        *varianceOut = 2.1981 * median_error_sqr;
                                }
                        }
                        else if(varianceOut)
                        {
                                // compute variance, which is the rms of reprojection errors
                                std::vector<cv::Point2f> imagePointsReproj;
                                cv::projectPoints(objectPoints, rvec, tvec, K, cv::Mat(), imagePointsReproj);
                                float err = 0.0f;
                                for(unsigned int i=0; i<inliers.size(); ++i)
                                {
                                        err += uNormSquared(imagePoints.at(inliers[i]).x - imagePointsReproj.at(inliers[i]).x, imagePoints.at(inliers[i]).y - imagePointsReproj.at(inliers[i]).y);
                                }
                                *varianceOut = std::sqrt(err/float(inliers.size()));
                        }
                }
        }

        if(matchesOut)
        {
                *matchesOut = matches;
        }
        if(inliersOut)
        {
                inliersOut->resize(inliers.size());
                for(unsigned int i=0; i<inliers.size(); ++i)
                {
                        inliersOut->at(i) = matches[inliers[i]];
                }
        }

        return transform;
}

Transform estimateMotion3DTo3D(
                        const std::map<int, cv::Point3f> & words3A,
                        const std::map<int, cv::Point3f> & words3B,
                        int minInliers,
                        double inliersDistance,
                        int iterations,
                        int refineIterations,
                        double * varianceOut,
                        std::vector<int> * matchesOut,
                        std::vector<int> * inliersOut)
{
        Transform transform;
        std::vector<cv::Point3f> inliers1; // previous
        std::vector<cv::Point3f> inliers2; // new

        std::vector<int> matches;
        util3d::findCorrespondences(
                        words3A,
                        words3B,
                        inliers1,
                        inliers2,
                        0,
                        &matches);
        UASSERT(inliers1.size() == inliers2.size());
        UDEBUG("Unique correspondences = %d", (int)inliers1.size());

        if(varianceOut)
        {
                *varianceOut = 1.0;
        }

        if((int)inliers1.size() >= minInliers)
        {
                std::vector<int> inliers;
                pcl::PointCloud<pcl::PointXYZ>::Ptr inliers1cloud(new pcl::PointCloud<pcl::PointXYZ>);
                pcl::PointCloud<pcl::PointXYZ>::Ptr inliers2cloud(new pcl::PointCloud<pcl::PointXYZ>);
                inliers1cloud->resize(inliers1.size());
                inliers2cloud->resize(inliers1.size());
                for(unsigned int i=0; i<inliers1.size(); ++i)
                {
                        (*inliers1cloud)[i].x = inliers1[i].x;
                        (*inliers1cloud)[i].y = inliers1[i].y;
                        (*inliers1cloud)[i].z = inliers1[i].z;
                        (*inliers2cloud)[i].x = inliers2[i].x;
                        (*inliers2cloud)[i].y = inliers2[i].y;
                        (*inliers2cloud)[i].z = inliers2[i].z;
                }
                Transform t = util3d::transformFromXYZCorrespondences(
                                inliers2cloud,
                                inliers1cloud,
                                inliersDistance,
                                iterations,
                                refineIterations,
                                3.0,
                                &inliers,
                                varianceOut);

                if(!t.isNull() && (int)inliers.size() >= minInliers)
                {
                        transform = t;
                }

                if(matchesOut)
                {
                        *matchesOut = matches;
                }

                if(inliersOut)
                {
                        inliersOut->resize(inliers.size());
                        for(unsigned int i=0; i<inliers.size(); ++i)
                        {
                                inliersOut->at(i) = matches[inliers[i]];
                        }
                }
        }
        return transform;
}


std::vector<float> computeReprojErrors(
                std::vector<cv::Point3f> opoints,
                std::vector<cv::Point2f> ipoints,
                const cv::Mat & cameraMatrix,
                const cv::Mat & distCoeffs,
                const cv::Mat & rvec,
                const cv::Mat & tvec,
                float reprojErrorThreshold,
                std::vector<int> & inliers)
{
        UASSERT(opoints.size() == ipoints.size());
        int count = (int)opoints.size();

        std::vector<cv::Point2f> projpoints;
        projectPoints(opoints, rvec, tvec, cameraMatrix, distCoeffs, projpoints);

        inliers.resize(count,0);
        std::vector<float> err(count);
        int oi=0;
        for (int i = 0; i < count; ++i)
        {
                float e = (float)cv::norm( ipoints[i] - projpoints[i]);
                if(e <= reprojErrorThreshold)
                {
                        inliers[oi] = i;
                        err[oi++] = e;
                }
        }
        inliers.resize(oi);
        err.resize(oi);
        return err;
}

void solvePnPRansac(
                const std::vector<cv::Point3f> & objectPoints,
                const std::vector<cv::Point2f> & imagePoints,
                const cv::Mat & cameraMatrix,
                const cv::Mat & distCoeffs,
                cv::Mat & rvec,
                cv::Mat & tvec,
                bool useExtrinsicGuess,
        int iterationsCount,
        float reprojectionError,
        int minInliersCount,
        std::vector<int> & inliers,
        int flags,
        int refineIterations,
        float refineSigma)
{
        if(minInliersCount < 4)
        {
                minInliersCount = 4;
        }
#if CV_MAJOR_VERSION < 3
        cv3::solvePnPRansac( //use OpenCV3 version of solvePnPRansac in OpenCV2
#else
        cv::solvePnPRansac( // use directly version from OpenCV 3
#endif
                        objectPoints,
                        imagePoints,
                        cameraMatrix,
                        distCoeffs,
                        rvec,
                        tvec,
                        useExtrinsicGuess,
                        iterationsCount,
                        reprojectionError,
                        0.99, // confidence
                        inliers,
                        flags);

        float inlierThreshold = reprojectionError;
        if((int)inliers.size() >= minInliersCount && refineIterations>0)
        {
                float error_threshold = inlierThreshold;
                int refine_iterations = 0;
                bool inlier_changed = false, oscillating = false;
                std::vector<int> new_inliers, prev_inliers = inliers;
                std::vector<size_t> inliers_sizes;
                //Eigen::VectorXf new_model_coefficients = model_coefficients;
                cv::Mat new_model_rvec = rvec;
                cv::Mat new_model_tvec = tvec;

                do
                {
                        // Get inliers from the current model
                        std::vector<cv::Point3f> opoints_inliers(prev_inliers.size());
                        std::vector<cv::Point2f> ipoints_inliers(prev_inliers.size());
                        for(unsigned int i=0; i<prev_inliers.size(); ++i)
                        {
                                opoints_inliers[i] = objectPoints[prev_inliers[i]];
                                ipoints_inliers[i] = imagePoints[prev_inliers[i]];
                        }

                        UDEBUG("inliers=%d refine_iterations=%d, rvec=%f,%f,%f tvec=%f,%f,%f", (int)prev_inliers.size(), refine_iterations,
                                        *new_model_rvec.ptr<double>(0), *new_model_rvec.ptr<double>(1), *new_model_rvec.ptr<double>(2),
                                        *new_model_tvec.ptr<double>(0), *new_model_tvec.ptr<double>(1), *new_model_tvec.ptr<double>(2));

                        // Optimize the model coefficients
                        cv::solvePnP(opoints_inliers, ipoints_inliers, cameraMatrix, distCoeffs, new_model_rvec, new_model_tvec, true, flags);
                        inliers_sizes.push_back(prev_inliers.size());

                        UDEBUG("rvec=%f,%f,%f tvec=%f,%f,%f",
                                        *new_model_rvec.ptr<double>(0), *new_model_rvec.ptr<double>(1), *new_model_rvec.ptr<double>(2),
                                        *new_model_tvec.ptr<double>(0), *new_model_tvec.ptr<double>(1), *new_model_tvec.ptr<double>(2));

                        // Select the new inliers based on the optimized coefficients and new threshold
                        std::vector<float> err = computeReprojErrors(objectPoints, imagePoints, cameraMatrix, distCoeffs, new_model_rvec, new_model_tvec, error_threshold, new_inliers);
                        UDEBUG("RANSAC refineModel: Number of inliers found (before/after): %d/%d, with an error threshold of %f.",
                                        (int)prev_inliers.size (), (int)new_inliers.size (), error_threshold);

                        if ((int)new_inliers.size() < minInliersCount)
                        {
                                ++refine_iterations;
                                if (refine_iterations >= refineIterations)
                                {
                                        break;
                                }
                                continue;
                        }

                        // Estimate the variance and the new threshold
                        float m = uMean(err.data(), err.size());
                        float variance = uVariance(err.data(), err.size());
                        error_threshold = std::min(inlierThreshold, refineSigma * float(sqrt(variance)));

                        UDEBUG ("RANSAC refineModel: New estimated error threshold: %f (variance=%f mean=%f) on iteration %d out of %d.",
                                  error_threshold, variance, m, refine_iterations, refineIterations);
                        inlier_changed = false;
                        std::swap (prev_inliers, new_inliers);

                        // If the number of inliers changed, then we are still optimizing
                        if (new_inliers.size () != prev_inliers.size ())
                        {
                                // Check if the number of inliers is oscillating in between two values
                                if ((int)inliers_sizes.size () >= minInliersCount)
                                {
                                        if (inliers_sizes[inliers_sizes.size () - 1] == inliers_sizes[inliers_sizes.size () - 3] &&
                                        inliers_sizes[inliers_sizes.size () - 2] == inliers_sizes[inliers_sizes.size () - 4])
                                        {
                                                oscillating = true;
                                                break;
                                        }
                                }
                                inlier_changed = true;
                                continue;
                        }

                        // Check the values of the inlier set
                        for (size_t i = 0; i < prev_inliers.size (); ++i)
                        {
                                // If the value of the inliers changed, then we are still optimizing
                                if (prev_inliers[i] != new_inliers[i])
                                {
                                        inlier_changed = true;
                                        break;
                                }
                        }
                }
                while (inlier_changed && ++refine_iterations < refineIterations);

                // If the new set of inliers is empty, we didn't do a good job refining
                if ((int)prev_inliers.size() < minInliersCount)
                {
                        UWARN ("RANSAC refineModel: Refinement failed: got very low inliers (%d)!", (int)prev_inliers.size());
                }

                if (oscillating)
                {
                        UDEBUG("RANSAC refineModel: Detected oscillations in the model refinement.");
                }

                std::swap (inliers, new_inliers);
                rvec = new_model_rvec;
                tvec = new_model_tvec;
        }

}

}

}