638 lines
22 KiB
C++
638 lines
22 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include <cmath>
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#include "ceres/autodiff_local_parameterization.h"
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#include "ceres/fpclassify.h"
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#include "ceres/householder_vector.h"
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#include "ceres/internal/autodiff.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/local_parameterization.h"
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#include "ceres/random.h"
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#include "ceres/rotation.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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TEST(IdentityParameterization, EverythingTest) {
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IdentityParameterization parameterization(3);
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EXPECT_EQ(parameterization.GlobalSize(), 3);
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EXPECT_EQ(parameterization.LocalSize(), 3);
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double x[3] = {1.0, 2.0, 3.0};
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double delta[3] = {0.0, 1.0, 2.0};
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double x_plus_delta[3] = {0.0, 0.0, 0.0};
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parameterization.Plus(x, delta, x_plus_delta);
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EXPECT_EQ(x_plus_delta[0], 1.0);
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EXPECT_EQ(x_plus_delta[1], 3.0);
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EXPECT_EQ(x_plus_delta[2], 5.0);
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double jacobian[9];
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parameterization.ComputeJacobian(x, jacobian);
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int k = 0;
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 3; ++j, ++k) {
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EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0);
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}
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}
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Matrix global_matrix = Matrix::Ones(10, 3);
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Matrix local_matrix = Matrix::Zero(10, 3);
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parameterization.MultiplyByJacobian(x,
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10,
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global_matrix.data(),
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local_matrix.data());
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EXPECT_EQ((local_matrix - global_matrix).norm(), 0.0);
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}
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TEST(SubsetParameterization, DeathTests) {
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std::vector<int> constant_parameters;
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EXPECT_DEATH_IF_SUPPORTED(
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SubsetParameterization parameterization(1, constant_parameters),
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"at least");
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constant_parameters.push_back(0);
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EXPECT_DEATH_IF_SUPPORTED(
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SubsetParameterization parameterization(1, constant_parameters),
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"Number of parameters");
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constant_parameters.push_back(1);
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EXPECT_DEATH_IF_SUPPORTED(
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SubsetParameterization parameterization(2, constant_parameters),
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"Number of parameters");
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constant_parameters.push_back(1);
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EXPECT_DEATH_IF_SUPPORTED(
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SubsetParameterization parameterization(2, constant_parameters),
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"duplicates");
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}
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TEST(SubsetParameterization, NormalFunctionTest) {
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const int kGlobalSize = 4;
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const int kLocalSize = 3;
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double x[kGlobalSize] = {1.0, 2.0, 3.0, 4.0};
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for (int i = 0; i < kGlobalSize; ++i) {
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std::vector<int> constant_parameters;
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constant_parameters.push_back(i);
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SubsetParameterization parameterization(kGlobalSize, constant_parameters);
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double delta[kLocalSize] = {1.0, 2.0, 3.0};
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double x_plus_delta[kGlobalSize] = {0.0, 0.0, 0.0};
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parameterization.Plus(x, delta, x_plus_delta);
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int k = 0;
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for (int j = 0; j < kGlobalSize; ++j) {
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if (j == i) {
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EXPECT_EQ(x_plus_delta[j], x[j]);
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} else {
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EXPECT_EQ(x_plus_delta[j], x[j] + delta[k++]);
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}
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}
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double jacobian[kGlobalSize * kLocalSize];
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parameterization.ComputeJacobian(x, jacobian);
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int delta_cursor = 0;
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int jacobian_cursor = 0;
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for (int j = 0; j < kGlobalSize; ++j) {
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if (j != i) {
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for (int k = 0; k < kLocalSize; ++k, jacobian_cursor++) {
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EXPECT_EQ(jacobian[jacobian_cursor], delta_cursor == k ? 1.0 : 0.0);
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}
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++delta_cursor;
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} else {
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for (int k = 0; k < kLocalSize; ++k, jacobian_cursor++) {
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EXPECT_EQ(jacobian[jacobian_cursor], 0.0);
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}
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}
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}
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Matrix global_matrix = Matrix::Ones(10, kGlobalSize);
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for (int row = 0; row < kGlobalSize; ++row) {
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for (int col = 0; col < kGlobalSize; ++col) {
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global_matrix(row, col) = col;
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}
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}
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Matrix local_matrix = Matrix::Zero(10, kLocalSize);
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parameterization.MultiplyByJacobian(x,
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10,
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global_matrix.data(),
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local_matrix.data());
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Matrix expected_local_matrix =
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global_matrix * MatrixRef(jacobian, kGlobalSize, kLocalSize);
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EXPECT_EQ((local_matrix - expected_local_matrix).norm(), 0.0);
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}
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}
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// Functor needed to implement automatically differentiated Plus for
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// quaternions.
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struct QuaternionPlus {
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template<typename T>
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bool operator()(const T* x, const T* delta, T* x_plus_delta) const {
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const T squared_norm_delta =
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delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2];
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T q_delta[4];
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if (squared_norm_delta > T(0.0)) {
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T norm_delta = sqrt(squared_norm_delta);
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const T sin_delta_by_delta = sin(norm_delta) / norm_delta;
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q_delta[0] = cos(norm_delta);
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q_delta[1] = sin_delta_by_delta * delta[0];
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q_delta[2] = sin_delta_by_delta * delta[1];
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q_delta[3] = sin_delta_by_delta * delta[2];
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} else {
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// We do not just use q_delta = [1,0,0,0] here because that is a
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// constant and when used for automatic differentiation will
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// lead to a zero derivative. Instead we take a first order
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// approximation and evaluate it at zero.
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q_delta[0] = T(1.0);
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q_delta[1] = delta[0];
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q_delta[2] = delta[1];
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q_delta[3] = delta[2];
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}
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QuaternionProduct(q_delta, x, x_plus_delta);
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return true;
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}
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};
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void QuaternionParameterizationTestHelper(const double* x,
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const double* delta,
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const double* q_delta) {
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const int kGlobalSize = 4;
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const int kLocalSize = 3;
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const double kTolerance = 1e-14;
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double x_plus_delta_ref[kGlobalSize] = {0.0, 0.0, 0.0, 0.0};
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QuaternionProduct(q_delta, x, x_plus_delta_ref);
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double x_plus_delta[kGlobalSize] = {0.0, 0.0, 0.0, 0.0};
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QuaternionParameterization parameterization;
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parameterization.Plus(x, delta, x_plus_delta);
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for (int i = 0; i < kGlobalSize; ++i) {
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EXPECT_NEAR(x_plus_delta[i], x_plus_delta_ref[i], kTolerance);
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}
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const double x_plus_delta_norm =
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sqrt(x_plus_delta[0] * x_plus_delta[0] +
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x_plus_delta[1] * x_plus_delta[1] +
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x_plus_delta[2] * x_plus_delta[2] +
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x_plus_delta[3] * x_plus_delta[3]);
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EXPECT_NEAR(x_plus_delta_norm, 1.0, kTolerance);
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double jacobian_ref[12];
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double zero_delta[kLocalSize] = {0.0, 0.0, 0.0};
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const double* parameters[2] = {x, zero_delta};
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double* jacobian_array[2] = { NULL, jacobian_ref };
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// Autodiff jacobian at delta_x = 0.
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internal::AutoDiff<QuaternionPlus,
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double,
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kGlobalSize,
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kLocalSize>::Differentiate(QuaternionPlus(),
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parameters,
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kGlobalSize,
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x_plus_delta,
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jacobian_array);
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double jacobian[12];
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parameterization.ComputeJacobian(x, jacobian);
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for (int i = 0; i < 12; ++i) {
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EXPECT_TRUE(IsFinite(jacobian[i]));
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EXPECT_NEAR(jacobian[i], jacobian_ref[i], kTolerance)
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<< "Jacobian mismatch: i = " << i
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<< "\n Expected \n"
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<< ConstMatrixRef(jacobian_ref, kGlobalSize, kLocalSize)
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<< "\n Actual \n"
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<< ConstMatrixRef(jacobian, kGlobalSize, kLocalSize);
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}
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Matrix global_matrix = Matrix::Random(10, kGlobalSize);
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Matrix local_matrix = Matrix::Zero(10, kLocalSize);
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parameterization.MultiplyByJacobian(x,
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10,
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global_matrix.data(),
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local_matrix.data());
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Matrix expected_local_matrix =
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global_matrix * MatrixRef(jacobian, kGlobalSize, kLocalSize);
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EXPECT_EQ((local_matrix - expected_local_matrix).norm(), 0.0);
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}
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template <int N>
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void Normalize(double* x) {
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VectorRef(x, N).normalize();
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}
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TEST(QuaternionParameterization, ZeroTest) {
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double x[4] = {0.5, 0.5, 0.5, 0.5};
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double delta[3] = {0.0, 0.0, 0.0};
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double q_delta[4] = {1.0, 0.0, 0.0, 0.0};
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QuaternionParameterizationTestHelper(x, delta, q_delta);
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}
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TEST(QuaternionParameterization, NearZeroTest) {
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double x[4] = {0.52, 0.25, 0.15, 0.45};
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Normalize<4>(x);
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double delta[3] = {0.24, 0.15, 0.10};
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for (int i = 0; i < 3; ++i) {
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delta[i] = delta[i] * 1e-14;
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}
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double q_delta[4];
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q_delta[0] = 1.0;
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q_delta[1] = delta[0];
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q_delta[2] = delta[1];
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q_delta[3] = delta[2];
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QuaternionParameterizationTestHelper(x, delta, q_delta);
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}
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TEST(QuaternionParameterization, AwayFromZeroTest) {
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double x[4] = {0.52, 0.25, 0.15, 0.45};
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Normalize<4>(x);
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double delta[3] = {0.24, 0.15, 0.10};
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const double delta_norm = sqrt(delta[0] * delta[0] +
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delta[1] * delta[1] +
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delta[2] * delta[2]);
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double q_delta[4];
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q_delta[0] = cos(delta_norm);
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q_delta[1] = sin(delta_norm) / delta_norm * delta[0];
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q_delta[2] = sin(delta_norm) / delta_norm * delta[1];
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q_delta[3] = sin(delta_norm) / delta_norm * delta[2];
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QuaternionParameterizationTestHelper(x, delta, q_delta);
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}
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// Functor needed to implement automatically differentiated Plus for
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// homogeneous vectors. Note this explicitly defined for vectors of size 4.
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struct HomogeneousVectorParameterizationPlus {
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template<typename Scalar>
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bool operator()(const Scalar* p_x, const Scalar* p_delta,
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Scalar* p_x_plus_delta) const {
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Eigen::Map<const Eigen::Matrix<Scalar, 4, 1> > x(p_x);
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Eigen::Map<const Eigen::Matrix<Scalar, 3, 1> > delta(p_delta);
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Eigen::Map<Eigen::Matrix<Scalar, 4, 1> > x_plus_delta(p_x_plus_delta);
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const Scalar squared_norm_delta =
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delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2];
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Eigen::Matrix<Scalar, 4, 1> y;
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Scalar one_half(0.5);
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if (squared_norm_delta > Scalar(0.0)) {
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Scalar norm_delta = sqrt(squared_norm_delta);
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Scalar norm_delta_div_2 = 0.5 * norm_delta;
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const Scalar sin_delta_by_delta = sin(norm_delta_div_2) /
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norm_delta_div_2;
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y[0] = sin_delta_by_delta * delta[0] * one_half;
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y[1] = sin_delta_by_delta * delta[1] * one_half;
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y[2] = sin_delta_by_delta * delta[2] * one_half;
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y[3] = cos(norm_delta_div_2);
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} else {
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// We do not just use y = [0,0,0,1] here because that is a
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// constant and when used for automatic differentiation will
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// lead to a zero derivative. Instead we take a first order
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// approximation and evaluate it at zero.
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y[0] = delta[0] * one_half;
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y[1] = delta[1] * one_half;
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y[2] = delta[2] * one_half;
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y[3] = Scalar(1.0);
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}
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Eigen::Matrix<Scalar, Eigen::Dynamic, 1> v(4);
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Scalar beta;
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internal::ComputeHouseholderVector<Scalar>(x, &v, &beta);
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x_plus_delta = x.norm() * (y - v * (beta * v.dot(y)));
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return true;
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}
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};
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void HomogeneousVectorParameterizationHelper(const double* x,
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const double* delta) {
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const double kTolerance = 1e-14;
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HomogeneousVectorParameterization homogeneous_vector_parameterization(4);
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// Ensure the update maintains the norm.
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double x_plus_delta[4] = {0.0, 0.0, 0.0, 0.0};
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homogeneous_vector_parameterization.Plus(x, delta, x_plus_delta);
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const double x_plus_delta_norm =
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sqrt(x_plus_delta[0] * x_plus_delta[0] +
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x_plus_delta[1] * x_plus_delta[1] +
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x_plus_delta[2] * x_plus_delta[2] +
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x_plus_delta[3] * x_plus_delta[3]);
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const double x_norm = sqrt(x[0] * x[0] + x[1] * x[1] +
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x[2] * x[2] + x[3] * x[3]);
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EXPECT_NEAR(x_plus_delta_norm, x_norm, kTolerance);
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// Autodiff jacobian at delta_x = 0.
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AutoDiffLocalParameterization<HomogeneousVectorParameterizationPlus, 4, 3>
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autodiff_jacobian;
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double jacobian_autodiff[12];
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double jacobian_analytic[12];
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homogeneous_vector_parameterization.ComputeJacobian(x, jacobian_analytic);
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autodiff_jacobian.ComputeJacobian(x, jacobian_autodiff);
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for (int i = 0; i < 12; ++i) {
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EXPECT_TRUE(ceres::IsFinite(jacobian_analytic[i]));
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EXPECT_NEAR(jacobian_analytic[i], jacobian_autodiff[i], kTolerance)
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<< "Jacobian mismatch: i = " << i << ", " << jacobian_analytic[i] << " "
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<< jacobian_autodiff[i];
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}
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}
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TEST(HomogeneousVectorParameterization, ZeroTest) {
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double x[4] = {0.0, 0.0, 0.0, 1.0};
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Normalize<4>(x);
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double delta[3] = {0.0, 0.0, 0.0};
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HomogeneousVectorParameterizationHelper(x, delta);
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}
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TEST(HomogeneousVectorParameterization, NearZeroTest1) {
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double x[4] = {1e-5, 1e-5, 1e-5, 1.0};
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Normalize<4>(x);
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double delta[3] = {0.0, 1.0, 0.0};
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HomogeneousVectorParameterizationHelper(x, delta);
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}
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TEST(HomogeneousVectorParameterization, NearZeroTest2) {
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double x[4] = {0.001, 0.0, 0.0, 0.0};
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double delta[3] = {0.0, 1.0, 0.0};
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HomogeneousVectorParameterizationHelper(x, delta);
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}
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TEST(HomogeneousVectorParameterization, AwayFromZeroTest1) {
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double x[4] = {0.52, 0.25, 0.15, 0.45};
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Normalize<4>(x);
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double delta[3] = {0.0, 1.0, -0.5};
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HomogeneousVectorParameterizationHelper(x, delta);
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}
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TEST(HomogeneousVectorParameterization, AwayFromZeroTest2) {
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double x[4] = {0.87, -0.25, -0.34, 0.45};
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Normalize<4>(x);
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double delta[3] = {0.0, 0.0, -0.5};
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HomogeneousVectorParameterizationHelper(x, delta);
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}
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TEST(HomogeneousVectorParameterization, AwayFromZeroTest3) {
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double x[4] = {0.0, 0.0, 0.0, 2.0};
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double delta[3] = {0.0, 0.0, 0};
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HomogeneousVectorParameterizationHelper(x, delta);
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}
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TEST(HomogeneousVectorParameterization, AwayFromZeroTest4) {
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double x[4] = {0.2, -1.0, 0.0, 2.0};
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double delta[3] = {1.4, 0.0, -0.5};
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HomogeneousVectorParameterizationHelper(x, delta);
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}
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TEST(HomogeneousVectorParameterization, AwayFromZeroTest5) {
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double x[4] = {2.0, 0.0, 0.0, 0.0};
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double delta[3] = {1.4, 0.0, -0.5};
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HomogeneousVectorParameterizationHelper(x, delta);
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}
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TEST(HomogeneousVectorParameterization, DeathTests) {
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EXPECT_DEATH_IF_SUPPORTED(HomogeneousVectorParameterization x(1), "size");
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}
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class ProductParameterizationTest : public ::testing::Test {
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protected :
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virtual void SetUp() {
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const int global_size1 = 5;
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std::vector<int> constant_parameters1;
|
|
constant_parameters1.push_back(2);
|
|
param1_.reset(new SubsetParameterization(global_size1,
|
|
constant_parameters1));
|
|
|
|
const int global_size2 = 3;
|
|
std::vector<int> constant_parameters2;
|
|
constant_parameters2.push_back(0);
|
|
constant_parameters2.push_back(1);
|
|
param2_.reset(new SubsetParameterization(global_size2,
|
|
constant_parameters2));
|
|
|
|
const int global_size3 = 4;
|
|
std::vector<int> constant_parameters3;
|
|
constant_parameters3.push_back(1);
|
|
param3_.reset(new SubsetParameterization(global_size3,
|
|
constant_parameters3));
|
|
|
|
const int global_size4 = 2;
|
|
std::vector<int> constant_parameters4;
|
|
constant_parameters4.push_back(1);
|
|
param4_.reset(new SubsetParameterization(global_size4,
|
|
constant_parameters4));
|
|
}
|
|
|
|
scoped_ptr<LocalParameterization> param1_;
|
|
scoped_ptr<LocalParameterization> param2_;
|
|
scoped_ptr<LocalParameterization> param3_;
|
|
scoped_ptr<LocalParameterization> param4_;
|
|
};
|
|
|
|
TEST_F(ProductParameterizationTest, LocalAndGlobalSize2) {
|
|
LocalParameterization* param1 = param1_.release();
|
|
LocalParameterization* param2 = param2_.release();
|
|
|
|
ProductParameterization product_param(param1, param2);
|
|
EXPECT_EQ(product_param.LocalSize(),
|
|
param1->LocalSize() + param2->LocalSize());
|
|
EXPECT_EQ(product_param.GlobalSize(),
|
|
param1->GlobalSize() + param2->GlobalSize());
|
|
}
|
|
|
|
|
|
TEST_F(ProductParameterizationTest, LocalAndGlobalSize3) {
|
|
LocalParameterization* param1 = param1_.release();
|
|
LocalParameterization* param2 = param2_.release();
|
|
LocalParameterization* param3 = param3_.release();
|
|
|
|
ProductParameterization product_param(param1, param2, param3);
|
|
EXPECT_EQ(product_param.LocalSize(),
|
|
param1->LocalSize() + param2->LocalSize() + param3->LocalSize());
|
|
EXPECT_EQ(product_param.GlobalSize(),
|
|
param1->GlobalSize() + param2->GlobalSize() + param3->GlobalSize());
|
|
}
|
|
|
|
TEST_F(ProductParameterizationTest, LocalAndGlobalSize4) {
|
|
LocalParameterization* param1 = param1_.release();
|
|
LocalParameterization* param2 = param2_.release();
|
|
LocalParameterization* param3 = param3_.release();
|
|
LocalParameterization* param4 = param4_.release();
|
|
|
|
ProductParameterization product_param(param1, param2, param3, param4);
|
|
EXPECT_EQ(product_param.LocalSize(),
|
|
param1->LocalSize() +
|
|
param2->LocalSize() +
|
|
param3->LocalSize() +
|
|
param4->LocalSize());
|
|
EXPECT_EQ(product_param.GlobalSize(),
|
|
param1->GlobalSize() +
|
|
param2->GlobalSize() +
|
|
param3->GlobalSize() +
|
|
param4->GlobalSize());
|
|
}
|
|
|
|
TEST_F(ProductParameterizationTest, Plus) {
|
|
LocalParameterization* param1 = param1_.release();
|
|
LocalParameterization* param2 = param2_.release();
|
|
LocalParameterization* param3 = param3_.release();
|
|
LocalParameterization* param4 = param4_.release();
|
|
|
|
ProductParameterization product_param(param1, param2, param3, param4);
|
|
std::vector<double> x(product_param.GlobalSize(), 0.0);
|
|
std::vector<double> delta(product_param.LocalSize(), 0.0);
|
|
std::vector<double> x_plus_delta_expected(product_param.GlobalSize(), 0.0);
|
|
std::vector<double> x_plus_delta(product_param.GlobalSize(), 0.0);
|
|
|
|
for (int i = 0; i < product_param.GlobalSize(); ++i) {
|
|
x[i] = RandNormal();
|
|
}
|
|
|
|
for (int i = 0; i < product_param.LocalSize(); ++i) {
|
|
delta[i] = RandNormal();
|
|
}
|
|
|
|
EXPECT_TRUE(product_param.Plus(&x[0], &delta[0], &x_plus_delta_expected[0]));
|
|
int x_cursor = 0;
|
|
int delta_cursor = 0;
|
|
|
|
EXPECT_TRUE(param1->Plus(&x[x_cursor],
|
|
&delta[delta_cursor],
|
|
&x_plus_delta[x_cursor]));
|
|
x_cursor += param1->GlobalSize();
|
|
delta_cursor += param1->LocalSize();
|
|
|
|
EXPECT_TRUE(param2->Plus(&x[x_cursor],
|
|
&delta[delta_cursor],
|
|
&x_plus_delta[x_cursor]));
|
|
x_cursor += param2->GlobalSize();
|
|
delta_cursor += param2->LocalSize();
|
|
|
|
EXPECT_TRUE(param3->Plus(&x[x_cursor],
|
|
&delta[delta_cursor],
|
|
&x_plus_delta[x_cursor]));
|
|
x_cursor += param3->GlobalSize();
|
|
delta_cursor += param3->LocalSize();
|
|
|
|
EXPECT_TRUE(param4->Plus(&x[x_cursor],
|
|
&delta[delta_cursor],
|
|
&x_plus_delta[x_cursor]));
|
|
x_cursor += param4->GlobalSize();
|
|
delta_cursor += param4->LocalSize();
|
|
|
|
for (int i = 0; i < x.size(); ++i) {
|
|
EXPECT_EQ(x_plus_delta[i], x_plus_delta_expected[i]);
|
|
}
|
|
}
|
|
|
|
TEST_F(ProductParameterizationTest, ComputeJacobian) {
|
|
LocalParameterization* param1 = param1_.release();
|
|
LocalParameterization* param2 = param2_.release();
|
|
LocalParameterization* param3 = param3_.release();
|
|
LocalParameterization* param4 = param4_.release();
|
|
|
|
ProductParameterization product_param(param1, param2, param3, param4);
|
|
std::vector<double> x(product_param.GlobalSize(), 0.0);
|
|
|
|
for (int i = 0; i < product_param.GlobalSize(); ++i) {
|
|
x[i] = RandNormal();
|
|
}
|
|
|
|
Matrix jacobian = Matrix::Random(product_param.GlobalSize(),
|
|
product_param.LocalSize());
|
|
EXPECT_TRUE(product_param.ComputeJacobian(&x[0], jacobian.data()));
|
|
int x_cursor = 0;
|
|
int delta_cursor = 0;
|
|
|
|
Matrix jacobian1(param1->GlobalSize(), param1->LocalSize());
|
|
EXPECT_TRUE(param1->ComputeJacobian(&x[x_cursor], jacobian1.data()));
|
|
jacobian.block(x_cursor, delta_cursor,
|
|
param1->GlobalSize(),
|
|
param1->LocalSize())
|
|
-= jacobian1;
|
|
x_cursor += param1->GlobalSize();
|
|
delta_cursor += param1->LocalSize();
|
|
|
|
Matrix jacobian2(param2->GlobalSize(), param2->LocalSize());
|
|
EXPECT_TRUE(param2->ComputeJacobian(&x[x_cursor], jacobian2.data()));
|
|
jacobian.block(x_cursor, delta_cursor,
|
|
param2->GlobalSize(),
|
|
param2->LocalSize())
|
|
-= jacobian2;
|
|
x_cursor += param2->GlobalSize();
|
|
delta_cursor += param2->LocalSize();
|
|
|
|
Matrix jacobian3(param3->GlobalSize(), param3->LocalSize());
|
|
EXPECT_TRUE(param3->ComputeJacobian(&x[x_cursor], jacobian3.data()));
|
|
jacobian.block(x_cursor, delta_cursor,
|
|
param3->GlobalSize(),
|
|
param3->LocalSize())
|
|
-= jacobian3;
|
|
x_cursor += param3->GlobalSize();
|
|
delta_cursor += param3->LocalSize();
|
|
|
|
Matrix jacobian4(param4->GlobalSize(), param4->LocalSize());
|
|
EXPECT_TRUE(param4->ComputeJacobian(&x[x_cursor], jacobian4.data()));
|
|
jacobian.block(x_cursor, delta_cursor,
|
|
param4->GlobalSize(),
|
|
param4->LocalSize())
|
|
-= jacobian4;
|
|
x_cursor += param4->GlobalSize();
|
|
delta_cursor += param4->LocalSize();
|
|
|
|
EXPECT_NEAR(jacobian.norm(), 0.0, std::numeric_limits<double>::epsilon());
|
|
}
|
|
|
|
} // namespace internal
|
|
} // namespace ceres
|