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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2014 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/local_parameterization.h"
#include "ceres/internal/eigen.h"
#include "ceres/rotation.h"
#include "glog/logging.h"
namespace ceres {
LocalParameterization::~LocalParameterization() {
}
bool LocalParameterization::MultiplyByJacobian(const double* x,
const int num_rows,
const double* global_matrix,
double* local_matrix) const {
Matrix jacobian(GlobalSize(), LocalSize());
if (!ComputeJacobian(x, jacobian.data())) {
return false;
}
MatrixRef(local_matrix, num_rows, LocalSize()) =
ConstMatrixRef(global_matrix, num_rows, GlobalSize()) * jacobian;
return true;
}
IdentityParameterization::IdentityParameterization(const int size)
: size_(size) {
CHECK_GT(size, 0);
}
bool IdentityParameterization::Plus(const double* x,
const double* delta,
double* x_plus_delta) const {
VectorRef(x_plus_delta, size_) =
ConstVectorRef(x, size_) + ConstVectorRef(delta, size_);
return true;
}
bool IdentityParameterization::ComputeJacobian(const double* x,
double* jacobian) const {
MatrixRef(jacobian, size_, size_) = Matrix::Identity(size_, size_);
return true;
}
bool IdentityParameterization::MultiplyByJacobian(const double* x,
const int num_cols,
const double* global_matrix,
double* local_matrix) const {
std::copy(global_matrix,
global_matrix + num_cols * GlobalSize(),
local_matrix);
return true;
}
SubsetParameterization::SubsetParameterization(
int size,
const vector<int>& constant_parameters)
: local_size_(size - constant_parameters.size()),
constancy_mask_(size, 0) {
CHECK_GT(constant_parameters.size(), 0)
<< "The set of constant parameters should contain at least "
<< "one element. If you do not wish to hold any parameters "
<< "constant, then do not use a SubsetParameterization";
vector<int> constant = constant_parameters;
sort(constant.begin(), constant.end());
CHECK(unique(constant.begin(), constant.end()) == constant.end())
<< "The set of constant parameters cannot contain duplicates";
CHECK_LT(constant_parameters.size(), size)
<< "Number of parameters held constant should be less "
<< "than the size of the parameter block. If you wish "
<< "to hold the entire parameter block constant, then a "
<< "efficient way is to directly mark it as constant "
<< "instead of using a LocalParameterization to do so.";
CHECK_GE(*min_element(constant.begin(), constant.end()), 0);
CHECK_LT(*max_element(constant.begin(), constant.end()), size);
for (int i = 0; i < constant_parameters.size(); ++i) {
constancy_mask_[constant_parameters[i]] = 1;
}
}
bool SubsetParameterization::Plus(const double* x,
const double* delta,
double* x_plus_delta) const {
for (int i = 0, j = 0; i < constancy_mask_.size(); ++i) {
if (constancy_mask_[i]) {
x_plus_delta[i] = x[i];
} else {
x_plus_delta[i] = x[i] + delta[j++];
}
}
return true;
}
bool SubsetParameterization::ComputeJacobian(const double* x,
double* jacobian) const {
MatrixRef m(jacobian, constancy_mask_.size(), local_size_);
m.setZero();
for (int i = 0, j = 0; i < constancy_mask_.size(); ++i) {
if (!constancy_mask_[i]) {
m(i, j++) = 1.0;
}
}
return true;
}
bool SubsetParameterization::MultiplyByJacobian(const double* x,
const int num_rows,
const double* global_matrix,
double* local_matrix) const {
for (int row = 0; row < num_rows; ++row) {
for (int col = 0, j = 0; col < constancy_mask_.size(); ++col) {
if (!constancy_mask_[col]) {
local_matrix[row * LocalSize() + j++] =
global_matrix[row * GlobalSize() + col];
}
}
}
return true;
}
bool QuaternionParameterization::Plus(const double* x,
const double* delta,
double* x_plus_delta) const {
const double norm_delta =
sqrt(delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2]);
if (norm_delta > 0.0) {
const double sin_delta_by_delta = (sin(norm_delta) / norm_delta);
double q_delta[4];
q_delta[0] = cos(norm_delta);
q_delta[1] = sin_delta_by_delta * delta[0];
q_delta[2] = sin_delta_by_delta * delta[1];
q_delta[3] = sin_delta_by_delta * delta[2];
QuaternionProduct(q_delta, x, x_plus_delta);
} else {
for (int i = 0; i < 4; ++i) {
x_plus_delta[i] = x[i];
}
}
return true;
}
bool QuaternionParameterization::ComputeJacobian(const double* x,
double* jacobian) const {
jacobian[0] = -x[1]; jacobian[1] = -x[2]; jacobian[2] = -x[3]; // NOLINT
jacobian[3] = x[0]; jacobian[4] = x[3]; jacobian[5] = -x[2]; // NOLINT
jacobian[6] = -x[3]; jacobian[7] = x[0]; jacobian[8] = x[1]; // NOLINT
jacobian[9] = x[2]; jacobian[10] = -x[1]; jacobian[11] = x[0]; // NOLINT
return true;
}
} // namespace ceres
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