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use OpenCV for SVD instead of eigen #9

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use OpenCV for SVD instead of eigen #9

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5 changes: 4 additions & 1 deletion CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,7 @@ find_package(PCL 1.2 REQUIRED)
find_package(Boost REQUIRED
COMPONENTS filesystem system signals regex date_time program_options thread
)
find_package(OpenCV REQUIRED)
# find_package(Eigen3)
#include(cmake)
# include(${CMAKE_CURRENT_SOURCE_DIR}/../A/CMakeLists.txt)
Expand Down Expand Up @@ -95,6 +96,7 @@ include_directories(
${Boost_INCLUDE_DIRS}
${EIGEN3_INCLUDE_DIRS}
${TBB_INCLUDE_DIRS}
${OpenCV_INCLUDE_DIRS}
)
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
Expand All @@ -111,4 +113,5 @@ target_link_libraries(lidartag_main
${PCL_LIBRARIES}
${NLOPT_LIBRARIES}
${TBB_LIBRARIES}
)
${OpenCV_LIBS}
)
4 changes: 4 additions & 0 deletions include/lidartag.h
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,10 @@
#include "types.h"
#include "thread_pool.h"

#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/core/eigen.hpp>

// #include "mat.h"
// #include "matrix.h"
// #include "tensorflow_ros_test/lib.h"
Expand Down
17 changes: 13 additions & 4 deletions src/lidartag.cc
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Expand Up @@ -3141,9 +3141,18 @@ namespace BipedLab {
ave.row(2) *= cluster.average.z;

Eigen::MatrixXf centered_data = cluster.merged_data.topRows(3) - ave;
Eigen::JacobiSVD<Eigen::MatrixXf> svd(centered_data, Eigen::ComputeFullU);
cluster.principal_axes = svd.matrixU();

//SVD using eigen
// Eigen::JacobiSVD<Eigen::MatrixXf> svd(centered_data, Eigen::ComputeFullU);
// cluster.principal_axes = svd.matrixU();

//SVD using OpenCV
cv::Mat cv_centered_data, cv_W, cv_U, cv_Vt;
cv::eigen2cv(centered_data, cv_centered_data);
cv::SVD::compute(cv_centered_data, cv_W, cv_U, cv_Vt);
Eigen::Matrix3f U;
cv::cv2eigen(cv_U, U);
cluster.principal_axes = U;
// Eigen::Matrix4f m1 = MatrixXd::Random(4,4);
// Eigen::Matrix4f m1;
// m1 << 1, 2, 3, 4,
Expand Down Expand Up @@ -3182,8 +3191,8 @@ namespace BipedLab {
pow(cluster.average.z, 2));
ROS_DEBUG_STREAM("Distance : " << distance);
ROS_DEBUG_STREAM("Actual Points: " << cluster.data.size() + cluster.edge_points.size());
Eigen::VectorXf sv = svd.singularValues();
ROS_DEBUG_STREAM("Singular values: " << sv[0] << ", " << sv[1] << ", " << sv[2]);
// Eigen::VectorXf sv = svd.singularValues();
// ROS_DEBUG_STREAM("Singular values: " << sv[0] << ", " << sv[1] << ", " << sv[2]);
}


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20 changes: 16 additions & 4 deletions src/utils.cc
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Expand Up @@ -462,10 +462,22 @@ void fitGrid_new(

Eigen::Matrix3f M =
GridVertices.rightCols(4)*payload_vertices.transpose();
Eigen::JacobiSVD<Eigen::MatrixXf> svd(
M, Eigen::ComputeFullU | Eigen::ComputeFullV);
Eigen::Matrix<float,3,3,Eigen::DontAlign> R =
svd.matrixU()*svd.matrixV().transpose();
Eigen::Matrix<float, 3, 3, Eigen::DontAlign> R;

//SVD using eigen
// Eigen::JacobiSVD<Eigen::MatrixXf> svd(
// M, Eigen::ComputeFullU | Eigen::ComputeFullV);
// R = svd.matrixU() * svd.matrixV().transpose();

//SVD using OpenCV
Eigen::Matrix3f r, U, V;
cv::Mat cv_A, cv_W, cv_U, cv_Vt;
cv::eigen2cv(M, cv_A);
cv::SVD::compute(cv_A, cv_W, cv_U, cv_Vt);
cv::cv2eigen(cv_W, r);
cv::cv2eigen(cv_U, U);
cv::cv2eigen(cv_Vt, V);
R = U * V;
H = R; // H: payload -> ref
}
velodyne_pointcloud::PointXYZIR toVelodyne(const Eigen::Vector3f &t_p){
Expand Down