702 lines
23 KiB
C++
702 lines
23 KiB
C++
#ifndef HANDEYECALIB_LIBRARY
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#define HANDEYECALIB_LIBRARY
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#endif
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#include "HandEyeCalib.h"
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#include "VrError.h"
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#include <Eigen/Dense>
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#include <Eigen/SVD>
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#include <cmath>
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HandEyeCalib::HandEyeCalib()
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{
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}
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HandEyeCalib::~HandEyeCalib()
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{
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}
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int HandEyeCalib::CalculateRT(
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const std::vector<HECPoint3D>& eyePoints,
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const std::vector<HECPoint3D>& robotPoints,
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HECCalibResult& result)
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{
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// 检查输入参数
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if (eyePoints.size() != robotPoints.size()) {
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return ERR_CODE(APP_ERR_PARAM);
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}
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int N = static_cast<int>(eyePoints.size());
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if (N < 3) {
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return ERR_CODE(APP_ERR_PARAM); // 至少需要3个点
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}
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// 【1】计算质心
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HECPoint3D p1, p2;
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for (int i = 0; i < N; i++) {
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p1 += eyePoints[i];
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p2 += robotPoints[i];
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}
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p1 = p1 / static_cast<double>(N);
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p2 = p2 / static_cast<double>(N);
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result.centerEye = p1;
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result.centerRobot = p2;
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// 【2】计算去中心坐标
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std::vector<HECPoint3D> q1(N), q2(N);
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for (int i = 0; i < N; i++) {
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q1[i] = eyePoints[i] - p1;
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q2[i] = robotPoints[i] - p2;
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}
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// 【3】计算协方差矩阵 W = sum(q1 * q2^T)
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Eigen::Matrix3d W = Eigen::Matrix3d::Zero();
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for (int i = 0; i < N; i++) {
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Eigen::Vector3d v1(q1[i].x, q1[i].y, q1[i].z);
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Eigen::Vector3d v2(q2[i].x, q2[i].y, q2[i].z);
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W += v1 * v2.transpose();
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}
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// 【4】对W进行SVD分解
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Eigen::JacobiSVD<Eigen::Matrix3d> svd(W, Eigen::ComputeFullU | Eigen::ComputeFullV);
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Eigen::Matrix3d U = svd.matrixU();
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Eigen::Matrix3d V = svd.matrixV();
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// 【5】计算旋转矩阵 R = V * M * U^T
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// M用于处理反射情况，确保R是正交旋转矩阵
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double det = (U * V.transpose()).determinant();
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Eigen::Matrix3d M;
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M << 1, 0, 0,
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0, 1, 0,
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0, 0, det;
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Eigen::Matrix3d R_eigen = V * M * U.transpose();
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// 【6】计算平移向量 T = p2 - R * p1
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Eigen::Vector3d p1_eigen(p1.x, p1.y, p1.z);
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Eigen::Vector3d p2_eigen(p2.x, p2.y, p2.z);
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Eigen::Vector3d T_eigen = p2_eigen - R_eigen * p1_eigen;
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// 【7】转换为输出格式
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 3; ++j) {
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result.R.at(i, j) = R_eigen(i, j);
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}
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}
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result.T = HECTranslationVector(T_eigen(0), T_eigen(1), T_eigen(2));
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// 【8】计算标定误差
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result.error = CalculateError(eyePoints, robotPoints, result);
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return SUCCESS;
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}
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int HandEyeCalib::CalculateRT(
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const std::vector<HECCalibPointPair>& pointPairs,
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HECCalibResult& result)
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{
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std::vector<HECPoint3D> eyePoints;
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std::vector<HECPoint3D> robotPoints;
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eyePoints.reserve(pointPairs.size());
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robotPoints.reserve(pointPairs.size());
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for (const auto& pair : pointPairs) {
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eyePoints.push_back(pair.eyePoint);
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robotPoints.push_back(pair.robotPoint);
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}
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return CalculateRT(eyePoints, robotPoints, result);
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}
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void HandEyeCalib::TransformPoint(
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const HECRotationMatrix& R,
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const HECTranslationVector& T,
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const HECPoint3D& srcPoint,
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HECPoint3D& dstPoint)
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{
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// 使用Eigen进行计算
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Eigen::Matrix3d R_eigen;
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 3; ++j) {
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R_eigen(i, j) = R.at(i, j);
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}
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}
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Eigen::Vector3d T_eigen(T.at(0), T.at(1), T.at(2));
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Eigen::Vector3d src_eigen(srcPoint.x, srcPoint.y, srcPoint.z);
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Eigen::Vector3d dst_eigen = R_eigen * src_eigen + T_eigen;
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dstPoint.x = dst_eigen(0);
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dstPoint.y = dst_eigen(1);
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dstPoint.z = dst_eigen(2);
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}
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void HandEyeCalib::TransformPointWithCenter(
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const HECRotationMatrix& R,
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const HECTranslationVector& T,
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const HECPoint3D& srcCenter,
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const HECPoint3D& dstCenter,
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const HECPoint3D& srcPoint,
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HECPoint3D& dstPoint)
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{
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// 使用Eigen进行计算
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Eigen::Matrix3d R_eigen;
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 3; ++j) {
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R_eigen(i, j) = R.at(i, j);
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}
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}
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Eigen::Vector3d srcCenter_eigen(srcCenter.x, srcCenter.y, srcCenter.z);
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Eigen::Vector3d dstCenter_eigen(dstCenter.x, dstCenter.y, dstCenter.z);
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Eigen::Vector3d src_eigen(srcPoint.x, srcPoint.y, srcPoint.z);
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// 变换公式: dstPoint = R * (srcPoint - srcCenter) + dstCenter
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Eigen::Vector3d dst_eigen = R_eigen * (src_eigen - srcCenter_eigen) + dstCenter_eigen;
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dstPoint.x = dst_eigen(0);
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dstPoint.y = dst_eigen(1);
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dstPoint.z = dst_eigen(2);
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}
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void HandEyeCalib::RotatePoint(
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const HECRotationMatrix& R,
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const HECPoint3D& srcPoint,
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HECPoint3D& dstPoint)
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{
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// 使用Eigen进行计算
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Eigen::Matrix3d R_eigen;
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 3; ++j) {
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R_eigen(i, j) = R.at(i, j);
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}
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}
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Eigen::Vector3d src_eigen(srcPoint.x, srcPoint.y, srcPoint.z);
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Eigen::Vector3d dst_eigen = R_eigen * src_eigen;
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dstPoint.x = dst_eigen(0);
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dstPoint.y = dst_eigen(1);
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dstPoint.z = dst_eigen(2);
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}
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void HandEyeCalib::RotationMatrixToEulerZYX(
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const HECRotationMatrix& R,
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HECEulerAngles& angles)
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{
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RotationMatrixToEuler(R, HECEulerOrder::ZYX, angles);
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}
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void HandEyeCalib::RotationMatrixToEuler(
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const HECRotationMatrix& R,
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HECEulerOrder order,
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HECEulerAngles& angles)
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{
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// 将 HECRotationMatrix 转换为 Eigen::Matrix3d
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Eigen::Matrix3d R_eigen;
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for (int i = 0; i < 3; ++i)
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for (int j = 0; j < 3; ++j)
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R_eigen(i, j) = R.at(i, j);
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// 外旋 ABC = 内旋 CBA，使用 Eigen 的 eulerAngles（内旋模式）
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Eigen::Vector3d ea;
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switch (order) {
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case HECEulerOrder::XYZ:
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// 外旋 XYZ = 内旋 ZYX
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ea = R_eigen.eulerAngles(2, 1, 0); // 返回 [yaw, pitch, roll]
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angles.yaw = ea[0]; angles.pitch = ea[1]; angles.roll = ea[2];
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break;
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case HECEulerOrder::XZY:
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// 外旋 XZY = 内旋 YZX
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ea = R_eigen.eulerAngles(1, 2, 0); // 返回 [pitch, yaw, roll]
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angles.pitch = ea[0]; angles.yaw = ea[1]; angles.roll = ea[2];
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break;
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case HECEulerOrder::YXZ:
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// 外旋 YXZ = 内旋 ZXY
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ea = R_eigen.eulerAngles(2, 0, 1); // 返回 [yaw, roll, pitch]
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angles.yaw = ea[0]; angles.roll = ea[1]; angles.pitch = ea[2];
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break;
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case HECEulerOrder::YZX:
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// 外旋 YZX = 内旋 XZY
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ea = R_eigen.eulerAngles(0, 2, 1); // 返回 [roll, yaw, pitch]
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angles.roll = ea[0]; angles.yaw = ea[1]; angles.pitch = ea[2];
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break;
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case HECEulerOrder::ZXY:
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// 外旋 ZXY = 内旋 YXZ
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ea = R_eigen.eulerAngles(1, 0, 2); // 返回 [pitch, roll, yaw]
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angles.pitch = ea[0]; angles.roll = ea[1]; angles.yaw = ea[2];
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break;
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case HECEulerOrder::ZYX:
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// 外旋 ZYX = 内旋 XYZ
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ea = R_eigen.eulerAngles(0, 1, 2); // 返回 [roll, pitch, yaw]
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angles.roll = ea[0]; angles.pitch = ea[1]; angles.yaw = ea[2];
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break;
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}
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}
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void HandEyeCalib::EulerZYXToRotationMatrix(
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const HECEulerAngles& angles,
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HECRotationMatrix& R)
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{
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EulerToRotationMatrix(angles, HECEulerOrder::ZYX, R);
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}
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void HandEyeCalib::EulerToRotationMatrix(
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const HECEulerAngles& angles,
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HECEulerOrder order,
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HECRotationMatrix& R)
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{
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double roll = angles.roll; // 绕X轴
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double pitch = angles.pitch; // 绕Y轴
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double yaw = angles.yaw; // 绕Z轴
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// 使用 Eigen::AngleAxisd 构造各轴旋转矩阵
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Eigen::Matrix3d Rx = Eigen::AngleAxisd(roll, Eigen::Vector3d::UnitX()).toRotationMatrix();
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Eigen::Matrix3d Ry = Eigen::AngleAxisd(pitch, Eigen::Vector3d::UnitY()).toRotationMatrix();
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Eigen::Matrix3d Rz = Eigen::AngleAxisd(yaw, Eigen::Vector3d::UnitZ()).toRotationMatrix();
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// 外旋 ABC：矩阵公式为 R = Rc · Rb · Ra（右乘，反向）
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Eigen::Matrix3d R_eigen;
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switch (order) {
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case HECEulerOrder::XYZ: R_eigen = Rz * Ry * Rx; break; // 外旋 XYZ
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case HECEulerOrder::XZY: R_eigen = Ry * Rz * Rx; break; // 外旋 XZY
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case HECEulerOrder::YXZ: R_eigen = Rz * Rx * Ry; break; // 外旋 YXZ
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case HECEulerOrder::YZX: R_eigen = Rx * Rz * Ry; break; // 外旋 YZX
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case HECEulerOrder::ZXY: R_eigen = Ry * Rx * Rz; break; // 外旋 ZXY
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case HECEulerOrder::ZYX: R_eigen = Rx * Ry * Rz; break; // 外旋 ZYX
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}
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// 转换回 HECRotationMatrix
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for (int i = 0; i < 3; ++i)
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for (int j = 0; j < 3; ++j)
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R.at(i, j) = R_eigen(i, j);
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}
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void HandEyeCalib::TransformPose(
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const HECCalibResult& calibResult,
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const HECPoint3D& eyePoint,
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const std::vector<HECPoint3D>& eyeDirVectors,
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bool invertYZ,
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HECPoseResult& poseResult)
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{
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// 1. 位置转换: P_robot = R * P_eye + T
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TransformPoint(calibResult.R, calibResult.T, eyePoint, poseResult.position);
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// 2. 姿态转换
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if (eyeDirVectors.size() < 3) {
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// 如果没有方向向量，姿态设为0
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poseResult.angles = HECEulerAngles();
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return;
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}
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// 复制方向向量
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std::vector<HECPoint3D> dirVectors = eyeDirVectors;
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// 如果需要，对Y轴和Z轴方向取反（坐标系方向调整）
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if (invertYZ) {
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dirVectors[1] = dirVectors[1] * (-1.0);
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dirVectors[2] = dirVectors[2] * (-1.0);
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}
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// 对每个方向向量应用旋转矩阵
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std::vector<HECPoint3D> robotDirVectors(3);
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for (int i = 0; i < 3; i++) {
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RotatePoint(calibResult.R, dirVectors[i], robotDirVectors[i]);
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}
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// 构建旋转矩阵（列向量形式）
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HECRotationMatrix R_pose;
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R_pose.at(0, 0) = robotDirVectors[0].x;
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R_pose.at(0, 1) = robotDirVectors[1].x;
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R_pose.at(0, 2) = robotDirVectors[2].x;
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R_pose.at(1, 0) = robotDirVectors[0].y;
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R_pose.at(1, 1) = robotDirVectors[1].y;
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R_pose.at(1, 2) = robotDirVectors[2].y;
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R_pose.at(2, 0) = robotDirVectors[0].z;
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R_pose.at(2, 1) = robotDirVectors[1].z;
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R_pose.at(2, 2) = robotDirVectors[2].z;
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// 从旋转矩阵提取欧拉角
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RotationMatrixToEulerZYX(R_pose, poseResult.angles);
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}
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double HandEyeCalib::CalculateError(
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const std::vector<HECPoint3D>& eyePoints,
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const std::vector<HECPoint3D>& robotPoints,
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const HECCalibResult& calibResult)
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{
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if (eyePoints.size() != robotPoints.size() || eyePoints.empty()) {
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return -1.0;
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}
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double totalError = 0.0;
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int N = static_cast<int>(eyePoints.size());
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for (int i = 0; i < N; i++) {
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HECPoint3D transformedPoint;
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TransformPoint(calibResult.R, calibResult.T, eyePoints[i], transformedPoint);
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HECPoint3D diff = transformedPoint - robotPoints[i];
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totalError += diff.norm();
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}
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return totalError / N;
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}
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int HandEyeCalib::CalculateEyeInHand(
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const std::vector<HECEyeInHandData>& calibData,
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HECCalibResult& result)
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{
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int N = static_cast<int>(calibData.size());
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if (N < 2) {
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return ERR_CODE(APP_ERR_PARAM);
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}
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// 眼在手上标定使用 AX=XB 问题的求解方法
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// A = T_end_i^{-1} * T_end_j (两次末端位姿之间的相对变换)
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// B = T_cam (相机观测到的标定点相对变换)
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// X = T_cam_to_end (待求的相机到末端的变换)
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// 构建方程组，使用最小二乘法求解
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// 这里使用简化方法：假设标定点固定，通过多组数据求解
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// 收集所有相机坐标系下的点，转换到基座坐标系
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// P_base = T_end * T_cam * P_cam
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// 对于固定标定点，所有 P_base 应该相同
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// 使用迭代优化方法求解
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// 初始估计：使用第一组数据
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Eigen::Matrix4d T_cam = Eigen::Matrix4d::Identity();
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// 构建超定方程组
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// 对于每对数据 (i, j)，有：
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// T_end_i * T_cam * P_cam_i = T_end_j * T_cam * P_cam_j
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// 即 T_end_i^{-1} * T_end_j * T_cam * P_cam_j = T_cam * P_cam_i
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// 使用 Tsai-Lenz 方法求解旋转部分
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std::vector<Eigen::Matrix3d> A_rot, B_rot;
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std::vector<Eigen::Vector3d> A_trans, B_trans;
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for (int i = 0; i < N - 1; i++) {
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// 获取末端位姿
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Eigen::Matrix4d T_end_i = Eigen::Matrix4d::Identity();
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Eigen::Matrix4d T_end_j = Eigen::Matrix4d::Identity();
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for (int r = 0; r < 4; r++) {
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for (int c = 0; c < 4; c++) {
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T_end_i(r, c) = calibData[i].endPose.at(r, c);
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T_end_j(r, c) = calibData[i + 1].endPose.at(r, c);
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}
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}
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// A = T_end_i^{-1} * T_end_j
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Eigen::Matrix4d A = T_end_i.inverse() * T_end_j;
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// 相机观测点
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Eigen::Vector3d P_cam_i(calibData[i].targetInCamera.x,
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calibData[i].targetInCamera.y,
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calibData[i].targetInCamera.z);
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Eigen::Vector3d P_cam_j(calibData[i + 1].targetInCamera.x,
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calibData[i + 1].targetInCamera.y,
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calibData[i + 1].targetInCamera.z);
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A_rot.push_back(A.block<3, 3>(0, 0));
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A_trans.push_back(A.block<3, 1>(0, 3));
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// B 矩阵从相机观测构建（假设标定点固定）
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// 这里简化处理，直接使用点的差异
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B_rot.push_back(Eigen::Matrix3d::Identity());
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B_trans.push_back(P_cam_i - P_cam_j);
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}
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// 使用 SVD 求解旋转矩阵
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// 构建 M 矩阵用于求解旋转
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Eigen::MatrixXd M(9 * (N - 1), 9);
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M.setZero();
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for (int i = 0; i < N - 1; i++) {
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// (A_rot ⊗ I - I ⊗ B_rot^T) * vec(X_rot) = 0
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Eigen::Matrix3d Ai = A_rot[i];
|
||
Eigen::Matrix3d Bi = B_rot[i];
|
||
|
||
for (int r = 0; r < 3; r++) {
|
||
for (int c = 0; c < 3; c++) {
|
||
// Kronecker product 展开
|
||
int row = i * 9 + r * 3 + c;
|
||
for (int k = 0; k < 3; k++) {
|
||
M(row, r * 3 + k) += Ai(c, k);
|
||
M(row, k * 3 + c) -= Bi(r, k);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
// SVD 求解
|
||
Eigen::JacobiSVD<Eigen::MatrixXd> svd(M, Eigen::ComputeFullV);
|
||
Eigen::VectorXd x = svd.matrixV().col(8);
|
||
|
||
// 重构旋转矩阵
|
||
Eigen::Matrix3d R_raw;
|
||
R_raw << x(0), x(1), x(2),
|
||
x(3), x(4), x(5),
|
||
x(6), x(7), x(8);
|
||
|
||
// 正交化旋转矩阵
|
||
Eigen::JacobiSVD<Eigen::Matrix3d> svd_R(R_raw, Eigen::ComputeFullU | Eigen::ComputeFullV);
|
||
Eigen::Matrix3d R_cam = svd_R.matrixU() * svd_R.matrixV().transpose();
|
||
|
||
// 确保行列式为1
|
||
if (R_cam.determinant() < 0) {
|
||
R_cam = -R_cam;
|
||
}
|
||
|
||
// 求解平移向量
|
||
// 使用最小二乘法: (A_rot - I) * t_cam = R_cam * B_trans - A_trans
|
||
Eigen::MatrixXd C(3 * (N - 1), 3);
|
||
Eigen::VectorXd d(3 * (N - 1));
|
||
|
||
for (int i = 0; i < N - 1; i++) {
|
||
C.block<3, 3>(i * 3, 0) = A_rot[i] - Eigen::Matrix3d::Identity();
|
||
d.segment<3>(i * 3) = R_cam * B_trans[i] - A_trans[i];
|
||
}
|
||
|
||
// 最小二乘求解
|
||
Eigen::Vector3d t_cam = C.bdcSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(d);
|
||
|
||
// 输出结果
|
||
for (int i = 0; i < 3; i++) {
|
||
for (int j = 0; j < 3; j++) {
|
||
result.R.at(i, j) = R_cam(i, j);
|
||
}
|
||
}
|
||
result.T = HECTranslationVector(t_cam(0), t_cam(1), t_cam(2));
|
||
|
||
// 计算误差
|
||
double totalError = 0.0;
|
||
Eigen::Matrix4d T_result = Eigen::Matrix4d::Identity();
|
||
T_result.block<3, 3>(0, 0) = R_cam;
|
||
T_result.block<3, 1>(0, 3) = t_cam;
|
||
|
||
// 计算标定点在基座坐标系下的位置（应该一致）
|
||
std::vector<Eigen::Vector3d> basePoints;
|
||
for (int i = 0; i < N; i++) {
|
||
Eigen::Matrix4d T_end = Eigen::Matrix4d::Identity();
|
||
for (int r = 0; r < 4; r++) {
|
||
for (int c = 0; c < 4; c++) {
|
||
T_end(r, c) = calibData[i].endPose.at(r, c);
|
||
}
|
||
}
|
||
|
||
Eigen::Vector4d P_cam(calibData[i].targetInCamera.x,
|
||
calibData[i].targetInCamera.y,
|
||
calibData[i].targetInCamera.z, 1.0);
|
||
Eigen::Vector4d P_base = T_end * T_result * P_cam;
|
||
basePoints.push_back(P_base.head<3>());
|
||
}
|
||
|
||
// 计算基座点的离散程度作为误差
|
||
Eigen::Vector3d meanBase = Eigen::Vector3d::Zero();
|
||
for (const auto& p : basePoints) {
|
||
meanBase += p;
|
||
}
|
||
meanBase /= N;
|
||
|
||
for (const auto& p : basePoints) {
|
||
totalError += (p - meanBase).norm();
|
||
}
|
||
result.error = totalError / N;
|
||
|
||
result.centerEye = HECPoint3D(0, 0, 0);
|
||
result.centerRobot = HECPoint3D(meanBase(0), meanBase(1), meanBase(2));
|
||
|
||
return SUCCESS;
|
||
}
|
||
|
||
int HandEyeCalib::CalculateEyeInHandWithTarget(
|
||
const std::vector<HECEyeInHandData>& calibData,
|
||
const HECPoint3D& targetInBase,
|
||
HECCalibResult& result)
|
||
{
|
||
int N = static_cast<int>(calibData.size());
|
||
if (N < 3) {
|
||
return ERR_CODE(APP_ERR_PARAM);
|
||
}
|
||
|
||
// 当标定点在基座坐标系下的位置已知时
|
||
// P_base = T_end * T_cam * P_cam
|
||
// T_cam = T_end^{-1} * T_base_to_cam
|
||
// 其中 T_base_to_cam 将 P_cam 变换到 P_base
|
||
|
||
// 收集相机坐标系下的点和对应的末端逆变换后的基座点
|
||
std::vector<HECPoint3D> camPoints;
|
||
std::vector<HECPoint3D> endPoints;
|
||
|
||
Eigen::Vector3d P_base(targetInBase.x, targetInBase.y, targetInBase.z);
|
||
|
||
for (int i = 0; i < N; i++) {
|
||
// 获取末端位姿的逆
|
||
Eigen::Matrix4d T_end = Eigen::Matrix4d::Identity();
|
||
for (int r = 0; r < 4; r++) {
|
||
for (int c = 0; c < 4; c++) {
|
||
T_end(r, c) = calibData[i].endPose.at(r, c);
|
||
}
|
||
}
|
||
Eigen::Matrix4d T_end_inv = T_end.inverse();
|
||
|
||
// 将基座点变换到末端坐标系
|
||
Eigen::Vector4d P_base_h(P_base(0), P_base(1), P_base(2), 1.0);
|
||
Eigen::Vector4d P_end = T_end_inv * P_base_h;
|
||
|
||
camPoints.push_back(calibData[i].targetInCamera);
|
||
endPoints.push_back(HECPoint3D(P_end(0), P_end(1), P_end(2)));
|
||
}
|
||
|
||
// 使用 SVD 方法求解相机到末端的变换
|
||
// 这与 EyeToHand 的 CalculateRT 方法相同
|
||
return CalculateRT(camPoints, endPoints, result);
|
||
}
|
||
|
||
Eigen::Matrix3d HandEyeCalib::eulerToRotationMatrix(double rx, double ry, double rz, HECEulerOrder order)
|
||
{
|
||
// 将角度转为弧度
|
||
const double deg2rad = M_PI / 180.0;
|
||
double roll = rx * deg2rad;
|
||
double pitch = ry * deg2rad;
|
||
double yaw = rz * deg2rad;
|
||
|
||
// 复用现有的 EulerToRotationMatrix 逻辑
|
||
HECEulerAngles angles(roll, pitch, yaw);
|
||
HECRotationMatrix R;
|
||
EulerToRotationMatrix(angles, order, R);
|
||
|
||
// 转换为 Eigen 矩阵
|
||
Eigen::Matrix3d result;
|
||
for (int i = 0; i < 3; ++i) {
|
||
for (int j = 0; j < 3; ++j) {
|
||
result(i, j) = R.at(i, j);
|
||
}
|
||
}
|
||
return result;
|
||
}
|
||
|
||
HECTCPCalibResult HandEyeCalib::CalculateTCP(const HECTCPCalibData& data)
|
||
{
|
||
HECTCPCalibResult result;
|
||
|
||
// 参数校验
|
||
int N = static_cast<int>(data.poses.size());
|
||
if (N < 3) {
|
||
result.success = false;
|
||
result.errorMessage = "至少需要3组法兰位姿数据";
|
||
return result;
|
||
}
|
||
|
||
// 构建每组位姿的旋转矩阵和位置向量
|
||
std::vector<Eigen::Matrix3d> rotations(N);
|
||
std::vector<Eigen::Vector3d> positions(N);
|
||
|
||
for (int i = 0; i < N; ++i) {
|
||
const auto& pose = data.poses[i];
|
||
rotations[i] = eulerToRotationMatrix(pose.rx, pose.ry, pose.rz, pose.eulerOrder);
|
||
positions[i] = Eigen::Vector3d(pose.x, pose.y, pose.z);
|
||
}
|
||
|
||
// === 3-DOF 位置求解 ===
|
||
// 约束：R_i * t_tcp + p_i = P_tcp（常数）
|
||
// 对相邻位姿对：(R_i - R_j) * t_tcp = p_j - p_i
|
||
int numPairs = N - 1;
|
||
Eigen::MatrixXd A(3 * numPairs, 3);
|
||
Eigen::VectorXd b(3 * numPairs);
|
||
|
||
for (int i = 0; i < numPairs; ++i) {
|
||
Eigen::Matrix3d dR = rotations[i] - rotations[i + 1];
|
||
Eigen::Vector3d dp = positions[i + 1] - positions[i];
|
||
|
||
A.block<3, 3>(i * 3, 0) = dR;
|
||
b.segment<3>(i * 3) = dp;
|
||
}
|
||
|
||
// SVD 最小二乘求解
|
||
Eigen::JacobiSVD<Eigen::MatrixXd> svd(A, Eigen::ComputeThinU | Eigen::ComputeThinV);
|
||
|
||
// 检查奇异值，判断退化情况
|
||
Eigen::VectorXd singularValues = svd.singularValues();
|
||
if (singularValues.size() < 3 || singularValues(2) < 1e-6) {
|
||
result.success = false;
|
||
result.errorMessage = "位姿数据退化，无法求解（可能所有位姿过于相似）";
|
||
return result;
|
||
}
|
||
|
||
Eigen::Vector3d t_tcp = svd.solve(b);
|
||
|
||
result.tx = t_tcp(0);
|
||
result.ty = t_tcp(1);
|
||
result.tz = t_tcp(2);
|
||
|
||
// 计算残差：所有位姿计算 R_i * t_tcp + p_i，应趋近同一点
|
||
Eigen::Vector3d meanTCPPoint = Eigen::Vector3d::Zero();
|
||
for (int i = 0; i < N; ++i) {
|
||
meanTCPPoint += rotations[i] * t_tcp + positions[i];
|
||
}
|
||
meanTCPPoint /= N;
|
||
|
||
double totalResidual = 0.0;
|
||
for (int i = 0; i < N; ++i) {
|
||
Eigen::Vector3d tcpPoint = rotations[i] * t_tcp + positions[i];
|
||
totalResidual += (tcpPoint - meanTCPPoint).squaredNorm();
|
||
}
|
||
result.residualError = std::sqrt(totalResidual / N);
|
||
|
||
// === 6-DOF 姿态求解 ===
|
||
if (data.mode == HECTCPCalibMode::Full6DOF) {
|
||
int refIdx = data.referencePoseIndex;
|
||
if (refIdx < 0 || refIdx >= N) {
|
||
result.success = false;
|
||
result.errorMessage = "参考位姿索引越界";
|
||
return result;
|
||
}
|
||
|
||
// 参考位姿的法兰旋转矩阵
|
||
Eigen::Matrix3d R_ref = rotations[refIdx];
|
||
|
||
// 期望的世界坐标系朝向旋转矩阵
|
||
Eigen::Matrix3d R_world = eulerToRotationMatrix(
|
||
data.worldRx, data.worldRy, data.worldRz, data.worldEulerOrder);
|
||
|
||
// TCP 相对于法兰的旋转：R_tcp = R_ref^T * R_world
|
||
Eigen::Matrix3d R_tcp = R_ref.transpose() * R_world;
|
||
|
||
// 将 R_tcp 转换为欧拉角（度）输出
|
||
HECRotationMatrix R_out;
|
||
for (int i = 0; i < 3; ++i) {
|
||
for (int j = 0; j < 3; ++j) {
|
||
R_out.at(i, j) = R_tcp(i, j);
|
||
}
|
||
}
|
||
|
||
HECEulerAngles tcpAngles;
|
||
RotationMatrixToEuler(R_out, data.worldEulerOrder, tcpAngles);
|
||
|
||
double rxDeg, ryDeg, rzDeg;
|
||
tcpAngles.toDegrees(rxDeg, ryDeg, rzDeg);
|
||
result.rx = rxDeg;
|
||
result.ry = ryDeg;
|
||
result.rz = rzDeg;
|
||
}
|
||
|
||
result.success = true;
|
||
return result;
|
||
}
|
||
|
||
// 导出函数实现
|
||
IHandEyeCalib* CreateHandEyeCalibInstance()
|
||
{
|
||
return new HandEyeCalib();
|
||
}
|
||
|
||
void DestroyHandEyeCalibInstance(IHandEyeCalib* instance)
|
||
{
|
||
delete instance;
|
||
}
|