bool MatrixTest::matrixAssignmentTests(void)
{
Matrix3f m;
m.setZero();
m(0, 0) = 1;
m(0, 1) = 2;
m(0, 2) = 3;
m(1, 0) = 4;
m(1, 1) = 5;
m(1, 2) = 6;
m(2, 0) = 7;
m(2, 1) = 8;
m(2, 2) = 9;
float data[9] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
Matrix3f m2(data);
double eps = 1e-6f;
for (int i = 0; i < 9; i++) {
ut_test(fabs(data[i] - m2.data()[i]) < eps);
}
float data_times_2[9] = {2, 4, 6, 8, 10, 12, 14, 16, 18};
Matrix3f m3(data_times_2);
ut_test(isEqual(m, m2));
ut_test(!isEqual(m, m3));
m2 *= 2;
ut_test(isEqual(m2, m3));
m2 /= 2;
m2 -= 1;
float data_minus_1[9] = {0, 1, 2, 3, 4, 5, 6, 7, 8};
ut_test(isEqual(Matrix3f(data_minus_1), m2));
m2 += 1;
ut_test(isEqual(Matrix3f(data), m2));
m3 -= m2;
ut_test(isEqual(m3, m2));
float data_row_02_swap[9] = {
7, 8, 9,
4, 5, 6,
1, 2, 3,
};
float data_col_02_swap[9] = {
3, 2, 1,
6, 5, 4,
9, 8, 7
};
Matrix3f m4(data);
ut_test(isEqual(-m4, m4 * (-1)));
m4.swapCols(0, 2);
ut_test(isEqual(m4, Matrix3f(data_col_02_swap)));
m4.swapCols(0, 2);
m4.swapRows(0, 2);
ut_test(isEqual(m4, Matrix3f(data_row_02_swap)));
ut_test(fabs(m4.min() - 1) < 1e-5);
Scalar<float> s;
s = 1;
ut_test(fabs(s - 1) < 1e-5);
Matrix<float, 1, 1> m5 = s;
ut_test(fabs(m5(0, 0) - s) < 1e-5);
Matrix<float, 2, 2> m6;
m6.setRow(0, Vector2f(1, 1));
m6.setCol(0, Vector2f(1, 1));
return true;
}
void CalibrationNode::performEstimation(){
if(rotationRB_vec.size() < 5 ){
std::cout << "Insufficient data" << std::endl;
return;
}
//perform least squares estimation
Matrix3f M;
Matrix4f rbi, rbj, cbi, cbj;
Matrix4f A, B;
Matrix3f I;
I.setIdentity();
MatrixXf C(0,3), bA(0,1), bB(0,1);
Vector3f ai, bi;
VectorXf V_tmp;
MatrixXf C_tmp;
M.setZero();
for(int i=0; i < (int)rotationRB_vec.size(); i++){
for(int j=0; j < (int)rotationRB_vec.size(); j++){
if(i!=j){
rbi << rotationRB_vec[i] , translationRB_vec[i] , 0, 0, 0, 1;
rbj << rotationRB_vec[j] , translationRB_vec[j] , 0, 0, 0, 1;
A = rbj.inverse()*rbi;
cbi << rotationCB_vec[i] , translationCB_vec[i] , 0, 0, 0, 1;
cbj << rotationCB_vec[j] , translationCB_vec[j] , 0, 0, 0, 1;
B = cbj*cbi.inverse();
ai = getLogTheta(A.block(0,0,3,3));
bi = getLogTheta(B.block(0,0,3,3));
M += bi*ai.transpose();
MatrixXf C_tmp = C;
C.resize(C.rows()+3, NoChange);
C << C_tmp, Matrix3f::Identity() - A.block(0,0,3,3);
V_tmp = bA;
bA.resize(bA.rows()+3, NoChange);
bA << V_tmp, A.block(0,3,3,1);
V_tmp = bB;
bB.resize(bB.rows()+3, NoChange);
bB << V_tmp, B.block(0,3,3,1);
}//end of if i!=j
}
}//end of for(.. i < rotationRB_vec.size(); ..)
#if ESTIMATION_DEBUG
std::cout << "M = [ " << M << " ]; " << endl;
#endif
EigenSolver<Matrix3f> es(M.transpose()*M);
Matrix3cf D = es.eigenvalues().asDiagonal();
Matrix3cf V = es.eigenvectors();
Matrix3cf Lambda = D.inverse().array().sqrt();
Matrix3cf Theta_X = V * Lambda * V.inverse() * M.transpose();
std::cout << "Orientation of Camera Frame with respect to Robot tool-tip frame." << std::endl;
std::cout << "Theta_X = [ " << Theta_X.real() << " ]; " << endl;
//Estimating translational offset
for(int i=0; i < bB.rows()/3; i++){
bB.block(i*3,0,3,1) = Theta_X.real()*bB.block(i*3,0,3,1);
}
bA = bA - bB; // this is d. saving memory
std::cout << "Translation of Camera Frame with respect to Robot tool-tip frame." << std::endl;
cout << "bX = [ " << (C.transpose()*C).inverse() * C.transpose() * bA << " ]; " << endl;
}
// assumes cv::Mats are of type <float>
int PoseEstimator::estimate(const matches_t &matches,
const cv::Mat &train_kpts, const cv::Mat &query_kpts,
const cv::Mat &train_pts, const cv::Mat &query_pts,
const cv::Mat &K, const double baseline)
{
// make sure we're using floats
if (train_kpts.depth() != CV_32F ||
query_kpts.depth() != CV_32F ||
train_pts.depth() != CV_32F ||
query_pts.depth() != CV_32F)
throw std::runtime_error("Expected input to be of floating point (CV_32F)");
int nmatch = matches.size();
cout << endl << "[pe3d] Matches: " << nmatch << endl;
// set up data structures for fast processing
// indices to good matches
std::vector<Eigen::Vector3f> t3d, q3d;
std::vector<Eigen::Vector2f> t2d, q2d;
std::vector<int> m; // keep track of matches
for (int i=0; i<nmatch; i++)
{
const float* ti = train_pts.ptr<float>(matches[i].trainIdx);
const float* qi = query_pts.ptr<float>(matches[i].queryIdx);
const float* ti2 = train_kpts.ptr<float>(matches[i].trainIdx);
const float* qi2 = query_kpts.ptr<float>(matches[i].queryIdx);
// make sure we have points within range; eliminates NaNs as well
if (matches[i].distance < 60 && // TODO: get this out of the estimator
ti[2] < maxMatchRange &&
qi[2] < maxMatchRange)
{
m.push_back(i);
t2d.push_back(Eigen::Vector2f(ti2[0],ti2[1]));
q2d.push_back(Eigen::Vector2f(qi2[0],qi2[1]));
t3d.push_back(Eigen::Vector3f(ti[0],ti[1],ti[2]));
q3d.push_back(Eigen::Vector3f(qi[0],qi[1],qi[2]));
}
}
nmatch = m.size();
cout << "[pe3d] Filtered matches: " << nmatch << endl;
if (nmatch < 3) return 0; // can't do it...
int bestinl = 0;
Matrix3f Kf;
cv::cv2eigen(K,Kf); // camera matrix
float fb = Kf(0,0)*baseline; // focal length times baseline
float maxInlierXDist2 = maxInlierXDist*maxInlierXDist;
float maxInlierDDist2 = maxInlierDDist*maxInlierDDist;
// set up minimum hyp pixel distance
float minpdist = minPDist * Kf(0,2) * 2.0;
// RANSAC loop
int numchoices = 1 + numRansac / 10;
for (int ii=0; ii<numRansac; ii++)
{
// find a candidate
int k;
int a=rand()%nmatch;
int b;
for (k=0; k<numchoices; k++)
{
b=rand()%nmatch;
if (a!=b && (t2d[a]-t2d[b]).norm() > minpdist
&& (q2d[a]-q2d[b]).norm() > minpdist)
// TODO: add distance check
break;
}
if (k >= numchoices) continue;
int c;
for (k=0; k<numchoices+numchoices; k++)
{
c=rand()%nmatch;
if (c!=b && c!=a && (t2d[a]-t2d[c]).norm() > minpdist
&& (t2d[b]-t2d[c]).norm() > minpdist
&& (q2d[a]-q2d[c]).norm() > minpdist
&& (q2d[b]-q2d[c]).norm() > minpdist)
// TODO: add distance check
break;
}
if (k >= numchoices+numchoices) continue;
// get centroids
Vector3f p0a = t3d[a];
Vector3f p0b = t3d[b];
Vector3f p0c = t3d[c];
Vector3f p1a = q3d[a];
Vector3f p1b = q3d[b];
Vector3f p1c = q3d[c];
Vector3f c0 = (p0a+p0b+p0c)*(1.0/3.0);
Vector3f c1 = (p1a+p1b+p1c)*(1.0/3.0);
// subtract out
p0a -= c0;
p0b -= c0;
p0c -= c0;
p1a -= c1;
p1b -= c1;
p1c -= c1;
Matrix3f Hf = p1a*p0a.transpose() + p1b*p0b.transpose() +
p1c*p0c.transpose();
Matrix3d H = Hf.cast<double>();
#if 0
cout << p0a.transpose() << endl;
cout << p0b.transpose() << endl;
cout << p0c.transpose() << endl;
#endif
// do the SVD thang
JacobiSVD<Matrix3d> svd(H, ComputeFullU | ComputeFullV);
Matrix3d V = svd.matrixV();
Matrix3d R = V * svd.matrixU().transpose();
double det = R.determinant();
//ntot++;
if (det < 0.0)
{
//nneg++;
V.col(2) = V.col(2)*-1.0;
R = V * svd.matrixU().transpose();
}
Vector3d cd0 = c0.cast<double>();
Vector3d cd1 = c1.cast<double>();
Vector3d tr = cd0-R*cd1; // translation
// cout << "[PE test] R: " << endl << R << endl;
// cout << "[PE test] t: " << tr.transpose() << endl;
Vector3f trf = tr.cast<float>(); // convert to floats
Matrix3f Rf = R.cast<float>();
Rf = Kf*Rf;
trf = Kf*trf;
// find inliers, based on image reprojection
int inl = 0;
for (int i=0; i<nmatch; i++)
{
const Vector3f &pt1 = q3d[i];
Vector3f ipt = Rf*pt1+trf; // transform query point
float iz = 1.0/ipt.z();
Vector2f &kp = t2d[i];
// cout << kp.transpose() << " " << pt1.transpose() << " " << ipt.transpose() << endl;
float dx = kp.x() - ipt.x()*iz;
float dy = kp.y() - ipt.y()*iz;
float dd = fb/t3d[i].z() - fb/ipt.z(); // diff of disparities, could pre-compute t3d
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2
&& dd*dd < maxInlierDDist2)
// inl+=(int)fsqrt(ipt.z()); // clever way to weight closer points
inl++;
}
if (inl > bestinl)
{
bestinl = inl;
trans = tr.cast<float>(); // convert to floats
rot = R.cast<float>();
}
}
cout << "[pe3d] Best inliers: " << bestinl << endl;
// printf("Total ransac: %d Neg det: %d\n", ntot, nneg);
// reduce matches to inliers
matches_t inls; // temporary for current inliers
inliers.clear();
Matrix3f Rf = Kf*rot;
Vector3f trf = Kf*trans;
// cout << "[pe3e] R: " << endl << rot << endl;
cout << "[pe3d] t: " << trans.transpose() << endl;
AngleAxisf aa;
aa.fromRotationMatrix(rot);
cout << "[pe3d] AA: " << aa.angle()*180.0/M_PI << " " << aa.axis().transpose() << endl;
for (int i=0; i<nmatch; i++)
{
Vector3f &pt1 = q3d[i];
Vector3f ipt = Rf*pt1+trf; // transform query point
float iz = 1.0/ipt.z();
Vector2f &kp = t2d[i];
// cout << kp.transpose() << " " << pt1.transpose() << " " << ipt.transpose() << endl;
float dx = kp.x() - ipt.x()*iz;
float dy = kp.y() - ipt.y()*iz;
float dd = fb/t3d[i].z() - fb/ipt.z(); // diff of disparities, could pre-compute t3d
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2 &&
dd*dd < maxInlierDDist2)
inls.push_back(matches[m[i]]);
}
cout << "[pe3d] Final inliers: " << inls.size() << endl;
// polish with SVD
if (polish)
{
Matrix3d Rd = rot.cast<double>();
Vector3d trd = trans.cast<double>();
StereoPolish pol(5,false);
pol.polish(inls,train_kpts,query_kpts,train_pts,query_pts,K,baseline,
Rd,trd);
AngleAxisd aa;
aa.fromRotationMatrix(Rd);
cout << "[pe3d] Polished t: " << trd.transpose() << endl;
cout << "[pe3d] Polished AA: " << aa.angle()*180.0/M_PI << " " << aa.axis().transpose() << endl;
int num = inls.size();
// get centroids
Vector3f c0(0,0,0);
Vector3f c1(0,0,0);
for (int i=0; i<num; i++)
{
c0 += t3d[i];
c1 += q3d[i];
}
c0 = c0 / (float)num;
c1 = c1 / (float)num;
// subtract out and create H matrix
Matrix3f Hf;
Hf.setZero();
for (int i=0; i<num; i++)
{
Vector3f p0 = t3d[i]-c0;
Vector3f p1 = q3d[i]-c1;
Hf += p1*p0.transpose();
}
Matrix3d H = Hf.cast<double>();
// do the SVD thang
JacobiSVD<Matrix3d> svd(H, ComputeFullU | ComputeFullV);
Matrix3d V = svd.matrixV();
Matrix3d R = V * svd.matrixU().transpose();
double det = R.determinant();
//ntot++;
if (det < 0.0)
{
//nneg++;
V.col(2) = V.col(2)*-1.0;
R = V * svd.matrixU().transpose();
}
Vector3d cd0 = c0.cast<double>();
Vector3d cd1 = c1.cast<double>();
Vector3d tr = cd0-R*cd1; // translation
aa.fromRotationMatrix(R);
cout << "[pe3d] t: " << tr.transpose() << endl;
cout << "[pe3d] AA: " << aa.angle()*180.0/M_PI << " " << aa.axis().transpose() << endl;
#if 0
// system
SysSBA sba;
sba.verbose = 0;
// set up nodes
// should have a frame => node function
Vector4d v0 = Vector4d(0,0,0,1);
Quaterniond q0 = Quaternion<double>(Vector4d(0,0,0,1));
sba.addNode(v0, q0, f0.cam, true);
Quaterniond qr1(rot); // from rotation matrix
Vector4d temptrans = Vector4d(trans(0), trans(1), trans(2), 1.0);
// sba.addNode(temptrans, qr1.normalized(), f1.cam, false);
qr1.normalize();
sba.addNode(temptrans, qr1, f1.cam, false);
int in = 3;
if (in > (int)inls.size())
in = inls.size();
// set up projections
for (int i=0; i<(int)inls.size(); i++)
{
// add point
int i0 = inls[i].queryIdx;
int i1 = inls[i].trainIdx;
Vector4d pt = query_pts[i0];
sba.addPoint(pt);
// projected point, ul,vl,ur
Vector3d ipt;
ipt(0) = f0.kpts[i0].pt.x;
ipt(1) = f0.kpts[i0].pt.y;
ipt(2) = ipt(0)-f0.disps[i0];
sba.addStereoProj(0, i, ipt);
// projected point, ul,vl,ur
ipt(0) = f1.kpts[i1].pt.x;
ipt(1) = f1.kpts[i1].pt.y;
ipt(2) = ipt(0)-f1.disps[i1];
sba.addStereoProj(1, i, ipt);
}
sba.huber = 2.0;
sba.doSBA(5,10e-4,SBA_DENSE_CHOLESKY);
int nbad = sba.removeBad(2.0);
// cout << endl << "Removed " << nbad << " projections > 2 pixels error" << endl;
sba.doSBA(5,10e-5,SBA_DENSE_CHOLESKY);
// cout << endl << sba.nodes[1].trans.transpose().head(3) << endl;
// get the updated transform
trans = sba.nodes[1].trans.head(3);
Quaterniond q1;
q1 = sba.nodes[1].qrot;
rot = q1.toRotationMatrix();
// set up inliers
inliers.clear();
for (int i=0; i<(int)inls.size(); i++)
{
ProjMap &prjs = sba.tracks[i].projections;
if (prjs[0].isValid && prjs[1].isValid) // valid track
inliers.push_back(inls[i]);
}
#if 0
printf("Inliers: %d After polish: %d\n", (int)inls.size(), (int)inliers.size());
#endif
#endif
}
inliers = inls;
return (int)inls.size();
}
int main()
{
Matrix3f m;
m.setZero();
m(0, 0) = 1;
m(0, 1) = 2;
m(0, 2) = 3;
m(1, 0) = 4;
m(1, 1) = 5;
m(1, 2) = 6;
m(2, 0) = 7;
m(2, 1) = 8;
m(2, 2) = 9;
float data[9] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
Matrix3f m2(data);
for(int i=0; i<9; i++) {
TEST(fabs(data[i] - m2.data()[i]) < 1e-6f);
}
float data_times_2[9] = {2, 4, 6, 8, 10, 12, 14, 16, 18};
Matrix3f m3(data_times_2);
TEST(isEqual(m, m2));
TEST(!(m == m3));
m2 *= 2;
TEST(m2 == m3);
m2 /= 2;
m2 -= 1;
float data_minus_1[9] = {0, 1, 2, 3, 4, 5, 6, 7, 8};
TEST(Matrix3f(data_minus_1) == m2);
m2 += 1;
TEST(Matrix3f(data) == m2);
m3 -= m2;
TEST(m3 == m2);
float data_row_02_swap[9] = {
7, 8, 9,
4, 5, 6,
1, 2, 3,
};
float data_col_02_swap[9] = {
3, 2, 1,
6, 5, 4,
9, 8, 7
};
Matrix3f m4(data);
TEST(-m4 == m4*(-1));
m4.swapCols(0, 2);
TEST(m4 == Matrix3f(data_col_02_swap));
m4.swapCols(0, 2);
m4.swapRows(0, 2);
TEST(m4 == Matrix3f(data_row_02_swap));
TEST(fabs(m4.min() - 1) < 1e-5);
Scalar<float> s;
s = 1;
TEST(fabs(s - 1) < 1e-5);
Matrix<float, 1, 1> m5 = s;
TEST(fabs(m5(0,0) - s) < 1e-5);
Matrix<float, 2, 2> m6;
m6.setRow(0, Vector2f(1, 1));
m6.setCol(0, Vector2f(1, 1));
return 0;
}
