void StateEstimatorKinematic::computeKss(const Eigen::Matrix<double,6,6> &A, const Eigen::Matrix<double,6,6> &C, int zDim )
{
Eigen::Matrix<double,6,6> B = C.transpose();
// Eigen::Matrix<double,6,6> P;
dare(A.transpose(), B, _P, zDim); // A^T is used here
_K.setZero();
Eigen::Matrix<double,6,6> PB;
Eigen::Matrix<double,6,6> BtPB_R;
if (zDim == 6) {
PB = _P * B;
BtPB_R = B.transpose() * PB + _R;
_K = PB* BtPB_R.inverse()
;
}
else
{
PB.topLeftCorner(6,zDim) = _P * B.topLeftCorner(6,zDim);
BtPB_R.topLeftCorner(zDim,zDim) = B.topLeftCorner(6,zDim).transpose() * PB.topLeftCorner(6,zDim) + _R.topLeftCorner(zDim,zDim);
_K.topLeftCorner(6,zDim) = PB.topLeftCorner(6,zDim)* BtPB_R.topLeftCorner(zDim,zDim).inverse();
}
}
void StateEstimatorKinematic::dare(const Eigen::Matrix<double,6,6> &A, const Eigen::Matrix<double,6,6> &B, Eigen::Matrix<double,6,6> &P,int zDim)
{
Eigen::Matrix<double,6,6> Ainv = A.inverse();
Eigen::Matrix<double,6,6> ABRB;
if (zDim == 6)
{
ABRB = Ainv * B * _R.llt().solve(B.transpose());
}
else {
ABRB = Ainv * B.topLeftCorner(6,zDim) * _R.topLeftCorner(zDim,zDim).llt().solve(B.topLeftCorner(6,zDim).transpose());
}
Eigen::Matrix<double,2*6,2*6> Z;
Z.block(0,0,6,6) = Ainv;
Z.block(0,6,6,6) = ABRB;
Z.block(6,0,6,6) = _Q * Ainv;
Z.block(6,6,6,6) = A.transpose() + _Q * ABRB;
Eigen::ComplexEigenSolver <Eigen::Matrix<double,2*6,2*6> > ces;
ces.compute(Z);
Eigen::Matrix<std::complex<double>,2*6,1> eigVal = ces.eigenvalues();
Eigen::Matrix<std::complex<double>,2*6,2*6> eigVec = ces.eigenvectors();
Eigen::Matrix<std::complex<double>,2*6,6> unstableEigVec;
int ctr = 0;
for (int i = 0; i < 2*6; i++) {
if (eigVal(i).real()*eigVal(i).real() + eigVal(i).imag()*eigVal(i).imag() > 1) {
unstableEigVec.col(ctr) = eigVec.col(i);
ctr++;
if (ctr > 6)
break;
}
}
Eigen::Matrix<std::complex<double>,6,6> U21inv = unstableEigVec.block(0,0,6,6).inverse();
Eigen::Matrix<std::complex<double>,6,6> PP = unstableEigVec.block(6,0,6,6) * U21inv;
for (int i = 0; i < 6; i++) {
for (int j = 0; j < 6; j++) {
P(i,j) = PP(i,j).real();
}
}
}
/* static */ bool ocraWbiConversions::wbiToOcraCoMJacobian(const Eigen::MatrixXd &jac, Eigen::Matrix<double,3,Eigen::Dynamic> &J)
{
if(DIM_T != jac.rows() || jac.cols() != J.cols())
{
std::cout<<"ERROR: Input and output matrices dimensions should be the same" <<std::endl;
return false;
}
Eigen::MatrixXd jac3;
Eigen::Matrix3d jac1,jac2;
jac3.resize(3,jac.cols()-6);
jac1 = jac.topLeftCorner(3,3);
jac2 = jac.block<3,3>(0,3);
jac3 = jac.topRightCorner(3,jac.cols()-6);
J.topLeftCorner(3,3) = jac2;
J.block<3,3>(0,3) = jac1;
J.topRightCorner(3,jac.cols()-6) = jac3;
return true;
}
void run ()
{
auto input_header = Header::open (argument[0]);
Header output_header (input_header);
output_header.datatype() = DataType::from_command_line (DataType::from<float> ());
// Linear
transform_type linear_transform;
bool linear = false;
auto opt = get_options ("linear");
if (opt.size()) {
linear = true;
linear_transform = load_transform (opt[0][0]);
} else {
linear_transform.setIdentity();
}
// Replace
const bool replace = get_options ("replace").size();
if (replace && !linear) {
INFO ("no linear is supplied so replace with the default (identity) transform");
linear = true;
}
// Template
opt = get_options ("template");
Header template_header;
if (opt.size()) {
if (replace)
throw Exception ("you cannot use the -replace option with the -template option");
template_header = Header::open (opt[0][0]);
for (size_t i = 0; i < 3; ++i) {
output_header.size(i) = template_header.size(i);
output_header.spacing(i) = template_header.spacing(i);
}
output_header.transform() = template_header.transform();
add_line (output_header.keyval()["comments"], std::string ("regridded to template image \"" + template_header.name() + "\""));
}
// Warp 5D warp
// TODO add reference to warp format documentation
opt = get_options ("warp_full");
Image<default_type> warp;
if (opt.size()) {
warp = Image<default_type>::open (opt[0][0]).with_direct_io();
if (warp.ndim() != 5)
throw Exception ("the input -warp_full image must be a 5D file.");
if (warp.size(3) != 3)
throw Exception ("the input -warp_full image must have 3 volumes (x,y,z) in the 4th dimension.");
if (warp.size(4) != 4)
throw Exception ("the input -warp_full image must have 4 volumes in the 5th dimension.");
if (linear)
throw Exception ("the -warp_full option cannot be applied in combination with -linear since the "
"linear transform is already included in the warp header");
}
// Warp from image1 or image2
int from = 1;
opt = get_options ("from");
if (opt.size()) {
from = opt[0][0];
if (!warp.valid())
WARN ("-from option ignored since no 5D warp was input");
}
// Warp deformation field
opt = get_options ("warp");
if (opt.size()) {
if (warp.valid())
throw Exception ("only one warp field can be input with either -warp or -warp_mid");
warp = Image<default_type>::open (opt[0][0]).with_direct_io (Stride::contiguous_along_axis(3));
if (warp.ndim() != 4)
throw Exception ("the input -warp file must be a 4D deformation field");
if (warp.size(3) != 3)
throw Exception ("the input -warp file must have 3 volumes in the 4th dimension (x,y,z positions)");
}
// Inverse
const bool inverse = get_options ("inverse").size();
if (inverse) {
if (!(linear || warp.valid()))
throw Exception ("no linear or warp transformation provided for option '-inverse'");
if (warp.valid())
if (warp.ndim() == 4)
throw Exception ("cannot apply -inverse with the input -warp_df deformation field.");
linear_transform = linear_transform.inverse();
}
// Half
const bool half = get_options ("half").size();
if (half) {
if (!(linear))
throw Exception ("no linear transformation provided for option '-half'");
{
Eigen::Matrix<default_type, 4, 4> temp;
temp.row(3) << 0, 0, 0, 1.0;
temp.topLeftCorner(3,4) = linear_transform.matrix().topLeftCorner(3,4);
linear_transform.matrix() = temp.sqrt().topLeftCorner(3,4);
}
}
// Flip
opt = get_options ("flip");
if (opt.size()) {
std::vector<int> axes = opt[0][0];
transform_type flip;
flip.setIdentity();
for (size_t i = 0; i < axes.size(); ++i) {
if (axes[i] < 0 || axes[i] > 2)
throw Exception ("axes supplied to -flip are out of bounds (" + std::string (opt[0][0]) + ")");
flip(axes[i],3) += flip(axes[i],axes[i]) * input_header.spacing(axes[i]) * (input_header.size(axes[i])-1);
flip(axes[i], axes[i]) *= -1.0;
}
if (!replace)
flip = input_header.transform() * flip * input_header.transform().inverse();
linear_transform = linear_transform * flip;
linear = true;
}
Stride::List stride = Stride::get (input_header);
// Detect FOD image for reorientation
opt = get_options ("noreorientation");
bool fod_reorientation = false;
Eigen::MatrixXd directions_cartesian;
if (!opt.size() && (linear || warp.valid() || template_header.valid()) && input_header.ndim() == 4 &&
input_header.size(3) >= 6 &&
input_header.size(3) == (int) Math::SH::NforL (Math::SH::LforN (input_header.size(3)))) {
CONSOLE ("SH series detected, performing apodised PSF reorientation");
fod_reorientation = true;
Eigen::MatrixXd directions_az_el;
opt = get_options ("directions");
if (opt.size())
directions_az_el = load_matrix (opt[0][0]);
else
directions_az_el = DWI::Directions::electrostatic_repulsion_300();
Math::SH::spherical2cartesian (directions_az_el, directions_cartesian);
// load with SH coeffients contiguous in RAM
stride = Stride::contiguous_along_axis (3, input_header);
}
// Modulate FODs
bool modulate = false;
if (get_options ("modulate").size()) {
modulate = true;
if (!fod_reorientation)
throw Exception ("modulation can only be performed with FOD reorientation");
}
// Rotate/Flip gradient directions if present
if (linear && input_header.ndim() == 4 && !warp && !fod_reorientation) {
try {
auto grad = DWI::get_DW_scheme (input_header);
if (input_header.size(3) == (ssize_t) grad.rows()) {
INFO ("DW gradients detected and will be reoriented");
Eigen::MatrixXd rotation = linear_transform.linear();
Eigen::MatrixXd test = rotation.transpose() * rotation;
test = test.array() / test.diagonal().mean();
if (!test.isIdentity (0.001))
WARN ("the input linear transform contains shear or anisotropic scaling and "
"therefore should not be used to reorient diffusion gradients");
if (replace)
rotation = linear_transform.linear() * input_header.transform().linear().inverse();
for (ssize_t n = 0; n < grad.rows(); ++n) {
Eigen::Vector3 grad_vector = grad.block<1,3>(n,0);
grad.block<1,3>(n,0) = rotation * grad_vector;
}
DWI::set_DW_scheme (output_header, grad);
}
}
catch (Exception& e) {
e.display (2);
}
}
// Interpolator
int interp = 2; // cubic
opt = get_options ("interp");
if (opt.size()) {
interp = opt[0][0];
if (!warp && !template_header)
WARN ("interpolator choice ignored since the input image will not be regridded");
}
// Out of bounds value
float out_of_bounds_value = 0.0;
opt = get_options ("nan");
if (opt.size()) {
out_of_bounds_value = NAN;
if (!warp && !template_header)
WARN ("Out of bounds value ignored since the input image will not be regridded");
}
auto input = input_header.get_image<float>().with_direct_io (stride);
// Reslice the image onto template
if (template_header.valid() && !warp) {
INFO ("image will be regridded");
if (get_options ("midway_space").size()) {
INFO("regridding to midway space");
std::vector<Header> headers;
headers.push_back(input_header);
headers.push_back(template_header);
std::vector<Eigen::Transform<default_type, 3, Eigen::Projective>> void_trafo;
auto padding = Eigen::Matrix<double, 4, 1>(1.0, 1.0, 1.0, 1.0);
int subsampling = 1;
auto midway_header = compute_minimum_average_header (headers, subsampling, padding, void_trafo);
for (size_t i = 0; i < 3; ++i) {
output_header.size(i) = midway_header.size(i);
output_header.spacing(i) = midway_header.spacing(i);
}
output_header.transform() = midway_header.transform();
}
if (interp == 0)
output_header.datatype() = DataType::from_command_line (input_header.datatype());
auto output = Image<float>::create (argument[1], output_header).with_direct_io();
switch (interp) {
case 0:
Filter::reslice<Interp::Nearest> (input, output, linear_transform, Adapter::AutoOverSample, out_of_bounds_value);
break;
case 1:
Filter::reslice<Interp::Linear> (input, output, linear_transform, Adapter::AutoOverSample, out_of_bounds_value);
break;
case 2:
Filter::reslice<Interp::Cubic> (input, output, linear_transform, Adapter::AutoOverSample, out_of_bounds_value);
break;
case 3:
Filter::reslice<Interp::Sinc> (input, output, linear_transform, Adapter::AutoOverSample, out_of_bounds_value);
break;
default:
assert (0);
break;
}
if (fod_reorientation)
Registration::Transform::reorient ("reorienting", output, output, linear_transform, directions_cartesian.transpose(), modulate);
} else if (warp.valid()) {
if (replace)
throw Exception ("you cannot use the -replace option with the -warp or -warp_df option");
if (!template_header) {
for (size_t i = 0; i < 3; ++i) {
output_header.size(i) = warp.size(i);
output_header.spacing(i) = warp.spacing(i);
}
output_header.transform() = warp.transform();
add_line (output_header.keyval()["comments"], std::string ("resliced using warp image \"" + warp.name() + "\""));
}
auto output = Image<float>::create(argument[1], output_header).with_direct_io();
if (warp.ndim() == 5) {
Image<default_type> warp_deform;
// Warp to the midway space defined by the warp grid
if (get_options ("midway_space").size()) {
warp_deform = Registration::Warp::compute_midway_deformation (warp, from);
// Use the full transform to warp from the image image to the template
} else {
warp_deform = Registration::Warp::compute_full_deformation (warp, template_header, from);
}
apply_warp (input, output, warp_deform, interp, out_of_bounds_value);
if (fod_reorientation)
Registration::Transform::reorient_warp ("reorienting", output, warp_deform, directions_cartesian.transpose(), modulate);
// Compose and apply input linear and 4D deformation field
} else if (warp.ndim() == 4 && linear) {
auto warp_composed = Image<default_type>::scratch (warp);
Registration::Warp::compose_linear_deformation (linear_transform, warp, warp_composed);
apply_warp (input, output, warp_composed, interp, out_of_bounds_value);
if (fod_reorientation)
Registration::Transform::reorient_warp ("reorienting", output, warp_composed, directions_cartesian.transpose(), modulate);
// Apply 4D deformation field only
} else {
apply_warp (input, output, warp, interp, out_of_bounds_value);
if (fod_reorientation)
Registration::Transform::reorient_warp ("reorienting", output, warp, directions_cartesian.transpose(), modulate);
}
// No reslicing required, so just modify the header and do a straight copy of the data
} else {
if (get_options ("midway").size())
throw Exception ("midway option given but no template image defined");
INFO ("image will not be regridded");
Eigen::MatrixXd rotation = linear_transform.linear();
Eigen::MatrixXd temp = rotation.transpose() * rotation;
if (!temp.isIdentity (0.001))
WARN("the input linear transform is not orthonormal and therefore applying this without the -template"
"option will mean the output header transform will also be not orthonormal");
add_line (output_header.keyval()["comments"], std::string ("transform modified"));
if (replace)
output_header.transform() = linear_transform;
else
output_header.transform() = linear_transform.inverse() * output_header.transform();
auto output = Image<float>::create (argument[1], output_header).with_direct_io();
copy_with_progress (input, output);
if (fod_reorientation) {
transform_type transform = linear_transform;
if (replace)
transform = linear_transform * output_header.transform().inverse();
Registration::Transform::reorient ("reorienting", output, output, transform, directions_cartesian.transpose());
}
}
}
void RGBDOdometry::getIncrementalTransformation(Eigen::Vector3f & trans,
Eigen::Matrix<float, 3, 3, Eigen::RowMajor> & rot,
const bool & rgbOnly,
const float & icpWeight,
const bool & pyramid,
const bool & fastOdom,
const bool & so3)
{
bool icp = !rgbOnly && icpWeight > 0;
bool rgb = rgbOnly || icpWeight < 100;
Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rprev = rot;
Eigen::Vector3f tprev = trans;
Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rcurr = Rprev;
Eigen::Vector3f tcurr = tprev;
if(rgb)
{
for(int i = 0; i < NUM_PYRS; i++)
{
computeDerivativeImages(nextImage[i], nextdIdx[i], nextdIdy[i]);
}
}
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> resultR = Eigen::Matrix<double, 3, 3, Eigen::RowMajor>::Identity();
if(so3)
{
int pyramidLevel = 2;
Eigen::Matrix<float, 3, 3, Eigen::RowMajor> R_lr = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>::Identity();
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> K = Eigen::Matrix<double, 3, 3, Eigen::RowMajor>::Zero();
K(0, 0) = intr(pyramidLevel).fx;
K(1, 1) = intr(pyramidLevel).fy;
K(0, 2) = intr(pyramidLevel).cx;
K(1, 2) = intr(pyramidLevel).cy;
K(2, 2) = 1;
float lastError = std::numeric_limits<float>::max() / 2;
float lastCount = std::numeric_limits<float>::max() / 2;
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> lastResultR = Eigen::Matrix<double, 3, 3, Eigen::RowMajor>::Identity();
for(int i = 0; i < 10; i++)
{
Eigen::Matrix<float, 3, 3, Eigen::RowMajor> jtj;
Eigen::Matrix<float, 3, 1> jtr;
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> homography = K * resultR * K.inverse();
mat33 imageBasis;
memcpy(&imageBasis.data[0], homography.cast<float>().eval().data(), sizeof(mat33));
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> K_inv = K.inverse();
mat33 kinv;
memcpy(&kinv.data[0], K_inv.cast<float>().eval().data(), sizeof(mat33));
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> K_R_lr = K * resultR;
mat33 krlr;
memcpy(&krlr.data[0], K_R_lr.cast<float>().eval().data(), sizeof(mat33));
float residual[2];
TICK("so3Step");
so3Step(lastNextImage[pyramidLevel],
nextImage[pyramidLevel],
imageBasis,
kinv,
krlr,
sumDataSO3,
outDataSO3,
jtj.data(),
jtr.data(),
&residual[0],
GPUConfig::getInstance().so3StepThreads,
GPUConfig::getInstance().so3StepBlocks);
TOCK("so3Step");
lastSO3Error = sqrt(residual[0]) / residual[1];
lastSO3Count = residual[1];
//Converged
if(lastSO3Error < lastError && lastCount == lastSO3Count)
{
break;
}
else if(lastSO3Error > lastError + 0.001) //Diverging
{
lastSO3Error = lastError;
lastSO3Count = lastCount;
resultR = lastResultR;
break;
}
lastError = lastSO3Error;
lastCount = lastSO3Count;
lastResultR = resultR;
Eigen::Vector3f delta = jtj.ldlt().solve(jtr);
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> rotUpdate = OdometryProvider::rodrigues(delta.cast<double>());
R_lr = rotUpdate.cast<float>() * R_lr;
for(int x = 0; x < 3; x++)
{
for(int y = 0; y < 3; y++)
{
resultR(x, y) = R_lr(x, y);
}
}
}
}
iterations[0] = fastOdom ? 3 : 10;
iterations[1] = pyramid ? 5 : 0;
iterations[2] = pyramid ? 4 : 0;
Eigen::Matrix<float, 3, 3, Eigen::RowMajor> Rprev_inv = Rprev.inverse();
mat33 device_Rprev_inv = Rprev_inv;
float3 device_tprev = *reinterpret_cast<float3*>(tprev.data());
Eigen::Matrix<double, 4, 4, Eigen::RowMajor> resultRt = Eigen::Matrix<double, 4, 4, Eigen::RowMajor>::Identity();
if(so3)
{
for(int x = 0; x < 3; x++)
{
for(int y = 0; y < 3; y++)
{
resultRt(x, y) = resultR(x, y);
}
}
}
for(int i = NUM_PYRS - 1; i >= 0; i--)
{
if(rgb)
{
projectToPointCloud(lastDepth[i], pointClouds[i], intr, i);
}
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> K = Eigen::Matrix<double, 3, 3, Eigen::RowMajor>::Zero();
K(0, 0) = intr(i).fx;
K(1, 1) = intr(i).fy;
K(0, 2) = intr(i).cx;
K(1, 2) = intr(i).cy;
K(2, 2) = 1;
lastRGBError = std::numeric_limits<float>::max();
for(int j = 0; j < iterations[i]; j++)
{
Eigen::Matrix<double, 4, 4, Eigen::RowMajor> Rt = resultRt.inverse();
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> R = Rt.topLeftCorner(3, 3);
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> KRK_inv = K * R * K.inverse();
mat33 krkInv;
memcpy(&krkInv.data[0], KRK_inv.cast<float>().eval().data(), sizeof(mat33));
Eigen::Vector3d Kt = Rt.topRightCorner(3, 1);
Kt = K * Kt;
float3 kt = {(float)Kt(0), (float)Kt(1), (float)Kt(2)};
int sigma = 0;
int rgbSize = 0;
if(rgb)
{
TICK("computeRgbResidual");
computeRgbResidual(pow(minimumGradientMagnitudes[i], 2.0) / pow(sobelScale, 2.0),
nextdIdx[i],
nextdIdy[i],
lastDepth[i],
nextDepth[i],
lastImage[i],
nextImage[i],
corresImg[i],
sumResidualRGB,
maxDepthDeltaRGB,
kt,
krkInv,
sigma,
rgbSize,
GPUConfig::getInstance().rgbResThreads,
GPUConfig::getInstance().rgbResBlocks);
TOCK("computeRgbResidual");
}
float sigmaVal = std::sqrt((float)sigma / rgbSize == 0 ? 1 : rgbSize);
float rgbError = std::sqrt(sigma) / (rgbSize == 0 ? 1 : rgbSize);
if(rgbOnly && rgbError > lastRGBError)
{
break;
}
lastRGBError = rgbError;
lastRGBCount = rgbSize;
if(rgbOnly)
{
sigmaVal = -1; //Signals the internal optimisation to weight evenly
}
Eigen::Matrix<float, 6, 6, Eigen::RowMajor> A_icp;
Eigen::Matrix<float, 6, 1> b_icp;
mat33 device_Rcurr = Rcurr;
float3 device_tcurr = *reinterpret_cast<float3*>(tcurr.data());
DeviceArray2D<float>& vmap_curr = vmaps_curr_[i];
DeviceArray2D<float>& nmap_curr = nmaps_curr_[i];
DeviceArray2D<float>& vmap_g_prev = vmaps_g_prev_[i];
DeviceArray2D<float>& nmap_g_prev = nmaps_g_prev_[i];
float residual[2];
if(icp)
{
TICK("icpStep");
icpStep(device_Rcurr,
device_tcurr,
vmap_curr,
nmap_curr,
device_Rprev_inv,
device_tprev,
intr(i),
vmap_g_prev,
nmap_g_prev,
distThres_,
angleThres_,
sumDataSE3,
outDataSE3,
A_icp.data(),
b_icp.data(),
&residual[0],
GPUConfig::getInstance().icpStepThreads,
GPUConfig::getInstance().icpStepBlocks);
TOCK("icpStep");
}
lastICPError = sqrt(residual[0]) / residual[1];
lastICPCount = residual[1];
Eigen::Matrix<float, 6, 6, Eigen::RowMajor> A_rgbd;
Eigen::Matrix<float, 6, 1> b_rgbd;
if(rgb)
{
TICK("rgbStep");
rgbStep(corresImg[i],
sigmaVal,
pointClouds[i],
intr(i).fx,
intr(i).fy,
nextdIdx[i],
nextdIdy[i],
sobelScale,
sumDataSE3,
outDataSE3,
A_rgbd.data(),
b_rgbd.data(),
GPUConfig::getInstance().rgbStepThreads,
GPUConfig::getInstance().rgbStepBlocks);
TOCK("rgbStep");
}
Eigen::Matrix<double, 6, 1> result;
Eigen::Matrix<double, 6, 6, Eigen::RowMajor> dA_rgbd = A_rgbd.cast<double>();
Eigen::Matrix<double, 6, 6, Eigen::RowMajor> dA_icp = A_icp.cast<double>();
Eigen::Matrix<double, 6, 1> db_rgbd = b_rgbd.cast<double>();
Eigen::Matrix<double, 6, 1> db_icp = b_icp.cast<double>();
if(icp && rgb)
{
double w = icpWeight;
lastA = dA_rgbd + w * w * dA_icp;
lastb = db_rgbd + w * db_icp;
result = lastA.ldlt().solve(lastb);
}
else if(icp)
{
lastA = dA_icp;
lastb = db_icp;
result = lastA.ldlt().solve(lastb);
}
else if(rgb)
{
lastA = dA_rgbd;
lastb = db_rgbd;
result = lastA.ldlt().solve(lastb);
}
else
{
assert(false && "Control shouldn't reach here");
}
Eigen::Isometry3f rgbOdom;
OdometryProvider::computeUpdateSE3(resultRt, result, rgbOdom);
Eigen::Isometry3f currentT;
currentT.setIdentity();
currentT.rotate(Rprev);
currentT.translation() = tprev;
currentT = currentT * rgbOdom.inverse();
tcurr = currentT.translation();
Rcurr = currentT.rotation();
}
}
if(rgb && (tcurr - tprev).norm() > 0.3)
{
Rcurr = Rprev;
tcurr = tprev;
}
if(so3)
{
for(int i = 0; i < NUM_PYRS; i++)
{
std::swap(lastNextImage[i], nextImage[i]);
}
}
trans = tcurr;
rot = Rcurr;
}
