minor output changes, added arrows for gradient and residual visualization
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@ -15,7 +15,7 @@
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#include <Eigen/Dense>
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#include <Eigen/Geometry>
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#include <Eigen/StdVector>
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#include <math.h>
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#include <visualization_msgs/Marker.h>
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#include <visualization_msgs/MarkerArray.h>
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#include <geometry_msgs/Point.h>
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@ -182,12 +182,14 @@ bool MarkerGeneration(
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const StateIDType& cam_state_id,
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CameraCalibration& cam0,
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const std::vector<double> photo_r,
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std::stringstream& ss) const;
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std::stringstream& ss,
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cv::Point2f gradientVector,
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cv::Point2f residualVector) const;
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/*
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* @brief projectPixelToPosition uses the calcualted pixels
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* @brief AnchorPixelToPosition uses the calcualted pixels
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* of the anchor patch to generate 3D positions of all of em
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*/
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inline Eigen::Vector3d projectPixelToPosition(cv::Point2f in_p,
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inline Eigen::Vector3d AnchorPixelToPosition(cv::Point2f in_p,
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const CameraCalibration& cam);
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/*
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@ -533,7 +535,9 @@ bool Feature::VisualizePatch(
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const StateIDType& cam_state_id,
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CameraCalibration& cam0,
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const std::vector<double> photo_r,
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std::stringstream& ss) const
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std::stringstream& ss,
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cv::Point2f gradientVector,
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cv::Point2f residualVector) const
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{
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double rescale = 1;
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@ -573,45 +577,107 @@ bool Feature::VisualizePatch(
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cv::hconcat(cam0.featureVisu, dottedFrame, cam0.featureVisu);
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// irradiance grid anchor
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// patches visualization
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int N = sqrt(anchorPatch_3d.size());
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int scale = 20;
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int scale = 30;
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cv::Mat irradianceFrame(anchorImage.size(), CV_8UC3, cv::Scalar(255, 240, 255));
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cv::resize(irradianceFrame, irradianceFrame, cv::Size(), rescale, rescale);
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// irradiance grid anchor
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std::stringstream namer;
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namer << "anchor";
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cv::putText(irradianceFrame, namer.str() , cvPoint(30, 25),
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cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0,0,0), 1, CV_AA);
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for(int i = 0; i<N; i++)
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for(int j = 0; j<N; j++)
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cv::rectangle(irradianceFrame,
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cv::Point(10+scale*(i+1), 10+scale*j),
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cv::Point(10+scale*i, 10+scale*(j+1)),
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cv::Point(30+scale*(i+1), 30+scale*j),
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cv::Point(30+scale*i, 30+scale*(j+1)),
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cv::Scalar(anchorPatch[i*N+j]*255, anchorPatch[i*N+j]*255, anchorPatch[i*N+j]*255),
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CV_FILLED);
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// irradiance grid projection
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namer.str(std::string());
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namer << "projection";
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cv::putText(irradianceFrame, namer.str() , cvPoint(30, 45+scale*N),
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cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0,0,0), 1, CV_AA);
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for(int i = 0; i<N; i++)
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for(int j = 0; j<N ; j++)
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cv::rectangle(irradianceFrame,
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cv::Point(10+scale*(i+1), 20+scale*(N+j)),
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cv::Point(10+scale*(i), 20+scale*(N+j+1)),
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cv::Point(30+scale*(i+1), 50+scale*(N+j)),
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cv::Point(30+scale*(i), 50+scale*(N+j+1)),
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cv::Scalar(projectionPatch[i*N+j]*255, projectionPatch[i*N+j]*255, projectionPatch[i*N+j]*255),
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CV_FILLED);
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// true irradiance at feature
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// get current observation
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namer.str(std::string());
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namer << "feature";
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cv::putText(irradianceFrame, namer.str() , cvPoint(30, 65+scale*2*N),
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cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0,0,0), 1, CV_AA);
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cv::Point2f p_f(observations.find(cam_state_id)->second(0),observations.find(cam_state_id)->second(1));
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// move to real pixels
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p_f = image_handler::distortPoint(p_f, cam0.intrinsics, cam0.distortion_model, cam0.distortion_coeffs);
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for(int i = 0; i<N; i++)
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{
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for(int j = 0; j<N ; j++)
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{
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float irr = PixelIrradiance(cv::Point2f(p_f.x + (i-(N-1)/2), p_f.y + (j-(N-1)/2)), current_image);
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cv::rectangle(irradianceFrame,
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cv::Point(30+scale*(i+1), 70+scale*(2*N+j)),
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cv::Point(30+scale*(i), 70+scale*(2*N+j+1)),
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cv::Scalar(irr*255, irr*255, irr*255),
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CV_FILLED);
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}
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}
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// residual grid projection, positive - red, negative - blue colored
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namer.str(std::string());
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namer << "residual";
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cv::putText(irradianceFrame, namer.str() , cvPoint(30+scale*N, scale*N/2-5),
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cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0,0,0), 1, CV_AA);
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for(int i = 0; i<N; i++)
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for(int j = 0; j<N; j++)
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if(photo_r[i*N+j]>0)
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cv::rectangle(irradianceFrame,
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cv::Point(20+scale*(N+i+1), 15+scale*(N/2+j)),
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cv::Point(20+scale*(N+i), 15+scale*(N/2+j+1)),
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cv::Point(40+scale*(N+i+1), 15+scale*(N/2+j)),
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cv::Point(40+scale*(N+i), 15+scale*(N/2+j+1)),
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cv::Scalar(255 - photo_r[i*N+j]*255, 255 - photo_r[i*N+j]*255, 255),
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CV_FILLED);
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else
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cv::rectangle(irradianceFrame,
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cv::Point(20+scale*(N+i+1), 15+scale*(N/2+j)),
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cv::Point(20+scale*(N+i), 15+scale*(N/2+j+1)),
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cv::Point(40+scale*(N+i+1), 15+scale*(N/2+j)),
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cv::Point(40+scale*(N+i), 15+scale*(N/2+j+1)),
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cv::Scalar(255, 255 + photo_r[i*N+j]*255, 255 + photo_r[i*N+j]*255),
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CV_FILLED);
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cv::hconcat(cam0.featureVisu, irradianceFrame, cam0.featureVisu);
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// gradient arrow
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/*
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cv::arrowedLine(irradianceFrame,
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cv::Point(30+scale*(N/2 +0.5), 50+scale*(N+(N/2)+0.5)),
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cv::Point(30+scale*(N/2+0.5)+scale*gradientVector.x, 50+scale*(N+(N/2)+0.5)+scale*gradientVector.y),
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cv::Scalar(100, 0, 255),
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1);
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*/
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// residual gradient direction
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cv::arrowedLine(irradianceFrame,
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cv::Point(40+scale*(N+N/2+0.5), 15+scale*((N-0.5))),
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cv::Point(40+scale*(N+N/2+0.5)+scale*residualVector.x, 15+scale*(N-0.5)+scale*residualVector.y),
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cv::Scalar(0, 255, 175),
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3);
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cv::hconcat(cam0.featureVisu, irradianceFrame, cam0.featureVisu);
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/*
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// visualize position of used observations and resulting feature position
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@ -703,7 +769,7 @@ cv::Point2f Feature::projectPositionToCamera(
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return my_p;
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}
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Eigen::Vector3d Feature::projectPixelToPosition(cv::Point2f in_p, const CameraCalibration& cam)
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Eigen::Vector3d Feature::AnchorPixelToPosition(cv::Point2f in_p, const CameraCalibration& cam)
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{
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// use undistorted position of point of interest
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// project it back into 3D space using pinhole model
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@ -742,27 +808,19 @@ bool Feature::initializeAnchor(const CameraCalibration& cam, int N)
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cv::Point2f und_pix_p = image_handler::distortPoint(cv::Point2f(u, v),
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cam.intrinsics,
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cam.distortion_model,
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0);
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cam.distortion_coeffs);
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// create vector of patch in pixel plane
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std::vector<cv::Point2f>und_pix_v;
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for(double u_run = -n; u_run <= n; u_run++)
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for(double v_run = -n; v_run <= n; v_run++)
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und_pix_v.push_back(cv::Point2f(und_pix_p.x+u_run, und_pix_p.y+v_run));
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anchorPatch_real.push_back(cv::Point2f(und_pix_p.x+u_run, und_pix_p.y+v_run));
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//create undistorted pure points
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std::vector<cv::Point2f> und_v;
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image_handler::undistortPoints(und_pix_v,
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image_handler::undistortPoints(anchorPatch_real,
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cam.intrinsics,
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cam.distortion_model,
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0,
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und_v);
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//create distorted pixel points
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anchorPatch_real = image_handler::distortPoints(und_v,
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cam.intrinsics,
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cam.distortion_model,
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cam.distortion_coeffs);
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cam.distortion_coeffs,
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anchorPatch_ideal);
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// save anchor position for later visualisaztion
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@ -770,19 +828,16 @@ bool Feature::initializeAnchor(const CameraCalibration& cam, int N)
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// save true pixel Patch position
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for(auto point : anchorPatch_real)
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{
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if(point.x - n < 0 || point.x + n >= cam.resolution(0) || point.y - n < 0 || point.y + n >= cam.resolution(1))
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return false;
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}
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for(auto point : anchorPatch_real)
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anchorPatch.push_back(PixelIrradiance(point, anchorImage));
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// project patch pixel to 3D space in camera coordinate system
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for(auto point : und_v)
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{
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anchorPatch_ideal.push_back(point);
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anchorPatch_3d.push_back(projectPixelToPosition(point, cam));
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}
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for(auto point : anchorPatch_ideal)
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anchorPatch_3d.push_back(AnchorPixelToPosition(point, cam));
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is_anchored = true;
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return true;
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}
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@ -14,7 +14,7 @@
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#include <string>
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#include <Eigen/Dense>
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#include <Eigen/Geometry>
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#include <math.h>
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#include <boost/shared_ptr.hpp>
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#include <opencv2/opencv.hpp>
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#include <opencv2/video.hpp>
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@ -38,6 +38,8 @@
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#include <message_filters/subscriber.h>
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#include <message_filters/time_synchronizer.h>
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#define PI 3.14159265
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namespace msckf_vio {
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/*
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* @brief MsckfVio Implements the algorithm in
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@ -24,7 +24,7 @@
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<param name="PrintImages" value="true"/>
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<param name="GroundTruth" value="false"/>
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<param name="patch_size_n" value="7"/>
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<param name="patch_size_n" value="5"/>
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<!-- Calibration parameters -->
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<rosparam command="load" file="$(arg calibration_file)"/>
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@ -1238,6 +1238,8 @@ void MsckfVio::PhotometricMeasurementJacobian(
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//photometric observation
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std::vector<double> photo_z;
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std::vector<double> photo_r;
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// individual Jacobians
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Matrix<double, 1, 2> dI_dhj = Matrix<double, 1, 2>::Zero();
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Matrix<double, 2, 3> dh_dCpij = Matrix<double, 2, 3>::Zero();
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@ -1262,9 +1264,31 @@ void MsckfVio::PhotometricMeasurementJacobian(
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auto frame = cam0.moving_window.find(cam_state_id)->second.image;
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//observation
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const Vector4d& z = feature.observations.find(cam_state_id)->second;
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//estimate photometric measurement
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std::vector<double> estimate_irradiance;
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std::vector<double> estimate_photo_z;
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IlluminationParameter estimated_illumination;
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feature.estimate_FrameIrradiance(cam_state, cam_state_id, cam0, estimate_irradiance, estimated_illumination);
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// calculated here, because we need true 'estimate_irradiance' later for jacobi
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for (auto& estimate_irradiance_j : estimate_irradiance)
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estimate_photo_z.push_back (estimate_irradiance_j *
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estimated_illumination.frame_gain * estimated_illumination.feature_gain +
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estimated_illumination.frame_bias + estimated_illumination.feature_bias);
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int count = 0;
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double dx, dy;
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// gradient visualization parameters
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cv::Point2f gradientVector(0,0);
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// residual change visualization
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cv::Point2f residualVector(0,0);
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double res_sum = 0;
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for (auto point : feature.anchorPatch_3d)
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{
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Eigen::Vector3d p_c0 = R_w_c0 * (point-t_c0_w);
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@ -1273,14 +1297,24 @@ void MsckfVio::PhotometricMeasurementJacobian(
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//add observation
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photo_z.push_back(feature.PixelIrradiance(p_in_c0, frame));
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// add jacobian
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//calculate photom. residual
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photo_r.push_back(photo_z[count] - estimate_photo_z[count]);
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// add jacobians
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// frame derivative calculated convoluting with kernel [-1, 0, 1]
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dx = feature.PixelIrradiance(cv::Point2f(p_in_c0.x+1, p_in_c0.y), frame) - feature.PixelIrradiance(cv::Point2f(p_in_c0.x-1, p_in_c0.y), frame);
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dy = feature.PixelIrradiance(cv::Point2f(p_in_c0.x, p_in_c0.y+1), frame) - feature.PixelIrradiance(cv::Point2f(p_in_c0.x, p_in_c0.y-1), frame);
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dI_dhj(0, 0) = dx;
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dI_dhj(0, 1) = dy;
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gradientVector.x += dx;
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gradientVector.y += dy;
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residualVector.x += dx * photo_r[count];
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residualVector.y += dy * photo_r[count];
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res_sum += photo_r[count];
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//dh / d{}^Cp_{ij}
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dh_dCpij(0, 0) = 1 / p_c0(2);
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dh_dCpij(1, 1) = 1 / p_c0(2);
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@ -1320,28 +1354,6 @@ void MsckfVio::PhotometricMeasurementJacobian(
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count++;
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}
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// calculate residual
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//observation
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const Vector4d& z = feature.observations.find(cam_state_id)->second;
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//estimate photometric measurement
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std::vector<double> estimate_irradiance;
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std::vector<double> estimate_photo_z;
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IlluminationParameter estimated_illumination;
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feature.estimate_FrameIrradiance(cam_state, cam_state_id, cam0, estimate_irradiance, estimated_illumination);
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// calculated here, because we need true 'estimate_irradiance' later for jacobi
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for (auto& estimate_irradiance_j : estimate_irradiance)
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estimate_photo_z.push_back (estimate_irradiance_j *
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estimated_illumination.frame_gain * estimated_illumination.feature_gain +
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estimated_illumination.frame_bias + estimated_illumination.feature_bias);
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std::vector<double> photo_r;
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//calculate photom. residual
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for(int i = 0; i < photo_z.size(); i++)
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photo_r.push_back(photo_z[i] - estimate_photo_z[i]);
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MatrixXd H_xl = MatrixXd::Zero(N*N, 21+state_server.cam_states.size()*7);
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MatrixXd H_yl = MatrixXd::Zero(N*N, N*N+state_server.cam_states.size()+1);
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@ -1379,12 +1391,13 @@ void MsckfVio::PhotometricMeasurementJacobian(
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count = 0;
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for(auto data : photo_r)
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r[count++] = data;
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std::stringstream ss;
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ss << "INFO:" << " anchor: " << cam_state_cntr_anchor << " frame: " << cam_state_cntr;
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if(PRINTIMAGES)
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{
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feature.MarkerGeneration(marker_pub, state_server.cam_states);
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feature.VisualizePatch(cam_state, cam_state_id, cam0, photo_r, ss);
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feature.VisualizePatch(cam_state, cam_state_id, cam0, photo_r, ss, gradientVector, residualVector);
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}
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return;
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@ -1481,13 +1494,11 @@ void MsckfVio::PhotometricFeatureJacobian(
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H_x = A_null_space.transpose() * H_xi;
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r = A_null_space.transpose() * r_i;
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ofstream myfile;
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myfile.open ("/home/raphael/dev/MSCKF_ws/log.txt");
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myfile << "-- residual -- \n" << r << "\n---- H ----\n" << H_x << "\n---- state cov ----\n" << state_server.state_cov <<endl;
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myfile << "Hx\n" << H_x << "r\n" << r << "from residual estimated error state: " << H_x. * r << endl;
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myfile.close();
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cout << "---------- LOGGED -------- " << endl;
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if(PRINTIMAGES)
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{
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@ -1631,13 +1642,11 @@ void MsckfVio::featureJacobian(
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ofstream myfile;
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myfile.open ("/home/raphael/dev/MSCKF_ws/log.txt");
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myfile << "-- residual -- \n" << r << "\n---- H ----\n" << H_x << "\n---- state cov ----\n" << state_server.state_cov <<endl;
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myfile << "Hx\n" << H_x << "r\n" << r << "from residual estimated error state: " << H_x.ldlt().solve(r) << endl;
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myfile.close();
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cout << "---------- LOGGED -------- " << endl;
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nh.setParam("/play_bag", false);
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return;
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}
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void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
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@ -1648,7 +1657,11 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
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// complexity as in Equation (28), (29).
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MatrixXd H_thin;
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VectorXd r_thin;
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int augmentationSize = 6;
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if(PHOTOMETRIC)
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augmentationSize = 7;
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|
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/*
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if (H.rows() > H.cols()) {
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// Convert H to a sparse matrix.
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SparseMatrix<double> H_sparse = H.sparseView();
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@ -1663,8 +1676,8 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
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(spqr_helper.matrixQ().transpose() * H).evalTo(H_temp);
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(spqr_helper.matrixQ().transpose() * r).evalTo(r_temp);
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H_thin = H_temp.topRows(21+state_server.cam_states.size()*6);
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r_thin = r_temp.head(21+state_server.cam_states.size()*6);
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H_thin = H_temp.topRows(21+state_server.cam_states.size()*augmentationSize);
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r_thin = r_temp.head(21+state_server.cam_states.size()*augmentationSize);
|
||||
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||||
//HouseholderQR<MatrixXd> qr_helper(H);
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||||
//MatrixXd Q = qr_helper.householderQ();
|
||||
@ -1676,18 +1689,19 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
|
||||
H_thin = H;
|
||||
r_thin = r;
|
||||
}
|
||||
*/
|
||||
|
||||
// Compute the Kalman gain.
|
||||
const MatrixXd& P = state_server.state_cov;
|
||||
MatrixXd S = H_thin*P*H_thin.transpose() +
|
||||
MatrixXd S = H*P*H.transpose() +
|
||||
Feature::observation_noise*MatrixXd::Identity(
|
||||
H_thin.rows(), H_thin.rows());
|
||||
//MatrixXd K_transpose = S.fullPivHouseholderQr().solve(H_thin*P);
|
||||
MatrixXd K_transpose = S.ldlt().solve(H_thin*P);
|
||||
H.rows(), H.rows());
|
||||
//MatrixXd K_transpose = S.fullPivHouseholderQr().solve(H*P);
|
||||
MatrixXd K_transpose = S.ldlt().solve(H*P);
|
||||
MatrixXd K = K_transpose.transpose();
|
||||
|
||||
// Compute the error of the state.
|
||||
VectorXd delta_x = K * r_thin;
|
||||
VectorXd delta_x = K * r;
|
||||
|
||||
// Update the IMU state.
|
||||
const VectorXd& delta_x_imu = delta_x.head<21>();
|
||||
@ -1722,7 +1736,7 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
|
||||
auto cam_state_iter = state_server.cam_states.begin();
|
||||
for (int i = 0; i < state_server.cam_states.size();
|
||||
++i, ++cam_state_iter) {
|
||||
const VectorXd& delta_x_cam = delta_x.segment<6>(21+i*6);
|
||||
const VectorXd& delta_x_cam = delta_x.segment(21+i*augmentationSize, augmentationSize);
|
||||
const Vector4d dq_cam = smallAngleQuaternion(delta_x_cam.head<3>());
|
||||
cam_state_iter->second.orientation = quaternionMultiplication(
|
||||
dq_cam, cam_state_iter->second.orientation);
|
||||
@ -1730,7 +1744,7 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
|
||||
}
|
||||
|
||||
// Update state covariance.
|
||||
MatrixXd I_KH = MatrixXd::Identity(K.rows(), H_thin.cols()) - K*H_thin;
|
||||
MatrixXd I_KH = MatrixXd::Identity(K.rows(), H.cols()) - K*H;
|
||||
//state_server.state_cov = I_KH*state_server.state_cov*I_KH.transpose() +
|
||||
// K*K.transpose()*Feature::observation_noise;
|
||||
state_server.state_cov = I_KH*state_server.state_cov;
|
||||
@ -1744,7 +1758,7 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
|
||||
}
|
||||
|
||||
bool MsckfVio::gatingTest(const MatrixXd& H, const VectorXd& r, const int& dof) {
|
||||
|
||||
return true;
|
||||
MatrixXd P1 = H * state_server.state_cov * H.transpose();
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user