mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Nikolaus Demmel
7b8f675bfd
- Change formatting standard to Cpp11. Main difference is not having the space between two closing >> for nested templates. We don't choose c++14, because older versions of clang-format (version 9 and earlier) don't know this value yet, and it doesn't make a difference in the formatting. - Apply clang-format to all (non generated) internal source files. - Manually fix some code sections (clang-format on/off) and c-strings - Exclude some embedded external files with very different formatting (gtest/gmock) - Add script to format all source files Change-Id: Ic6cea41575ad6e37c9e136dbce176b0d505dc44d
516 lines
16 KiB
C++
516 lines
16 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
|
|
// Copyright 2015 Google Inc. All rights reserved.
|
|
// http://ceres-solver.org/
|
|
//
|
|
// 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)
|
|
// mierle@gmail.com (Keir Mierle)
|
|
|
|
#include "ceres/dynamic_numeric_diff_cost_function.h"
|
|
|
|
#include <cstddef>
|
|
#include <memory>
|
|
|
|
#include "gtest/gtest.h"
|
|
|
|
namespace ceres {
|
|
namespace internal {
|
|
|
|
using std::vector;
|
|
|
|
const double kTolerance = 1e-6;
|
|
|
|
// Takes 2 parameter blocks:
|
|
// parameters[0] is size 10.
|
|
// parameters[1] is size 5.
|
|
// Emits 21 residuals:
|
|
// A: i - parameters[0][i], for i in [0,10) -- this is 10 residuals
|
|
// B: parameters[0][i] - i, for i in [0,10) -- this is another 10.
|
|
// C: sum(parameters[0][i]^2 - 8*parameters[0][i]) + sum(parameters[1][i])
|
|
class MyCostFunctor {
|
|
public:
|
|
bool operator()(double const* const* parameters, double* residuals) const {
|
|
const double* params0 = parameters[0];
|
|
int r = 0;
|
|
for (int i = 0; i < 10; ++i) {
|
|
residuals[r++] = i - params0[i];
|
|
residuals[r++] = params0[i] - i;
|
|
}
|
|
|
|
double c_residual = 0.0;
|
|
for (int i = 0; i < 10; ++i) {
|
|
c_residual += pow(params0[i], 2) - 8.0 * params0[i];
|
|
}
|
|
|
|
const double* params1 = parameters[1];
|
|
for (int i = 0; i < 5; ++i) {
|
|
c_residual += params1[i];
|
|
}
|
|
residuals[r++] = c_residual;
|
|
return true;
|
|
}
|
|
};
|
|
|
|
TEST(DynamicNumericdiffCostFunctionTest, TestResiduals) {
|
|
vector<double> param_block_0(10, 0.0);
|
|
vector<double> param_block_1(5, 0.0);
|
|
DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
|
|
new MyCostFunctor());
|
|
cost_function.AddParameterBlock(param_block_0.size());
|
|
cost_function.AddParameterBlock(param_block_1.size());
|
|
cost_function.SetNumResiduals(21);
|
|
|
|
// Test residual computation.
|
|
vector<double> residuals(21, -100000);
|
|
vector<double*> parameter_blocks(2);
|
|
parameter_blocks[0] = ¶m_block_0[0];
|
|
parameter_blocks[1] = ¶m_block_1[0];
|
|
EXPECT_TRUE(
|
|
cost_function.Evaluate(¶meter_blocks[0], residuals.data(), NULL));
|
|
for (int r = 0; r < 10; ++r) {
|
|
EXPECT_EQ(1.0 * r, residuals.at(r * 2));
|
|
EXPECT_EQ(-1.0 * r, residuals.at(r * 2 + 1));
|
|
}
|
|
EXPECT_EQ(0, residuals.at(20));
|
|
}
|
|
|
|
TEST(DynamicNumericdiffCostFunctionTest, TestJacobian) {
|
|
// Test the residual counting.
|
|
vector<double> param_block_0(10, 0.0);
|
|
for (int i = 0; i < 10; ++i) {
|
|
param_block_0[i] = 2 * i;
|
|
}
|
|
vector<double> param_block_1(5, 0.0);
|
|
DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
|
|
new MyCostFunctor());
|
|
cost_function.AddParameterBlock(param_block_0.size());
|
|
cost_function.AddParameterBlock(param_block_1.size());
|
|
cost_function.SetNumResiduals(21);
|
|
|
|
// Prepare the residuals.
|
|
vector<double> residuals(21, -100000);
|
|
|
|
// Prepare the parameters.
|
|
vector<double*> parameter_blocks(2);
|
|
parameter_blocks[0] = ¶m_block_0[0];
|
|
parameter_blocks[1] = ¶m_block_1[0];
|
|
|
|
// Prepare the jacobian.
|
|
vector<vector<double>> jacobian_vect(2);
|
|
jacobian_vect[0].resize(21 * 10, -100000);
|
|
jacobian_vect[1].resize(21 * 5, -100000);
|
|
vector<double*> jacobian;
|
|
jacobian.push_back(jacobian_vect[0].data());
|
|
jacobian.push_back(jacobian_vect[1].data());
|
|
|
|
// Test jacobian computation.
|
|
EXPECT_TRUE(cost_function.Evaluate(
|
|
parameter_blocks.data(), residuals.data(), jacobian.data()));
|
|
|
|
for (int r = 0; r < 10; ++r) {
|
|
EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
|
|
EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
|
|
}
|
|
EXPECT_EQ(420, residuals.at(20));
|
|
for (int p = 0; p < 10; ++p) {
|
|
// Check "A" Jacobian.
|
|
EXPECT_NEAR(-1.0, jacobian_vect[0][2 * p * 10 + p], kTolerance);
|
|
// Check "B" Jacobian.
|
|
EXPECT_NEAR(+1.0, jacobian_vect[0][(2 * p + 1) * 10 + p], kTolerance);
|
|
jacobian_vect[0][2 * p * 10 + p] = 0.0;
|
|
jacobian_vect[0][(2 * p + 1) * 10 + p] = 0.0;
|
|
}
|
|
|
|
// Check "C" Jacobian for first parameter block.
|
|
for (int p = 0; p < 10; ++p) {
|
|
EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance);
|
|
jacobian_vect[0][20 * 10 + p] = 0.0;
|
|
}
|
|
for (int i = 0; i < jacobian_vect[0].size(); ++i) {
|
|
EXPECT_NEAR(0.0, jacobian_vect[0][i], kTolerance);
|
|
}
|
|
|
|
// Check "C" Jacobian for second parameter block.
|
|
for (int p = 0; p < 5; ++p) {
|
|
EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance);
|
|
jacobian_vect[1][20 * 5 + p] = 0.0;
|
|
}
|
|
for (int i = 0; i < jacobian_vect[1].size(); ++i) {
|
|
EXPECT_NEAR(0.0, jacobian_vect[1][i], kTolerance);
|
|
}
|
|
}
|
|
|
|
TEST(DynamicNumericdiffCostFunctionTest,
|
|
JacobianWithFirstParameterBlockConstant) { // NOLINT
|
|
// Test the residual counting.
|
|
vector<double> param_block_0(10, 0.0);
|
|
for (int i = 0; i < 10; ++i) {
|
|
param_block_0[i] = 2 * i;
|
|
}
|
|
vector<double> param_block_1(5, 0.0);
|
|
DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
|
|
new MyCostFunctor());
|
|
cost_function.AddParameterBlock(param_block_0.size());
|
|
cost_function.AddParameterBlock(param_block_1.size());
|
|
cost_function.SetNumResiduals(21);
|
|
|
|
// Prepare the residuals.
|
|
vector<double> residuals(21, -100000);
|
|
|
|
// Prepare the parameters.
|
|
vector<double*> parameter_blocks(2);
|
|
parameter_blocks[0] = ¶m_block_0[0];
|
|
parameter_blocks[1] = ¶m_block_1[0];
|
|
|
|
// Prepare the jacobian.
|
|
vector<vector<double>> jacobian_vect(2);
|
|
jacobian_vect[0].resize(21 * 10, -100000);
|
|
jacobian_vect[1].resize(21 * 5, -100000);
|
|
vector<double*> jacobian;
|
|
jacobian.push_back(NULL);
|
|
jacobian.push_back(jacobian_vect[1].data());
|
|
|
|
// Test jacobian computation.
|
|
EXPECT_TRUE(cost_function.Evaluate(
|
|
parameter_blocks.data(), residuals.data(), jacobian.data()));
|
|
|
|
for (int r = 0; r < 10; ++r) {
|
|
EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
|
|
EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
|
|
}
|
|
EXPECT_EQ(420, residuals.at(20));
|
|
|
|
// Check "C" Jacobian for second parameter block.
|
|
for (int p = 0; p < 5; ++p) {
|
|
EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance);
|
|
jacobian_vect[1][20 * 5 + p] = 0.0;
|
|
}
|
|
for (int i = 0; i < jacobian_vect[1].size(); ++i) {
|
|
EXPECT_EQ(0.0, jacobian_vect[1][i]);
|
|
}
|
|
}
|
|
|
|
TEST(DynamicNumericdiffCostFunctionTest,
|
|
JacobianWithSecondParameterBlockConstant) { // NOLINT
|
|
// Test the residual counting.
|
|
vector<double> param_block_0(10, 0.0);
|
|
for (int i = 0; i < 10; ++i) {
|
|
param_block_0[i] = 2 * i;
|
|
}
|
|
vector<double> param_block_1(5, 0.0);
|
|
DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(
|
|
new MyCostFunctor());
|
|
cost_function.AddParameterBlock(param_block_0.size());
|
|
cost_function.AddParameterBlock(param_block_1.size());
|
|
cost_function.SetNumResiduals(21);
|
|
|
|
// Prepare the residuals.
|
|
vector<double> residuals(21, -100000);
|
|
|
|
// Prepare the parameters.
|
|
vector<double*> parameter_blocks(2);
|
|
parameter_blocks[0] = ¶m_block_0[0];
|
|
parameter_blocks[1] = ¶m_block_1[0];
|
|
|
|
// Prepare the jacobian.
|
|
vector<vector<double>> jacobian_vect(2);
|
|
jacobian_vect[0].resize(21 * 10, -100000);
|
|
jacobian_vect[1].resize(21 * 5, -100000);
|
|
vector<double*> jacobian;
|
|
jacobian.push_back(jacobian_vect[0].data());
|
|
jacobian.push_back(NULL);
|
|
|
|
// Test jacobian computation.
|
|
EXPECT_TRUE(cost_function.Evaluate(
|
|
parameter_blocks.data(), residuals.data(), jacobian.data()));
|
|
|
|
for (int r = 0; r < 10; ++r) {
|
|
EXPECT_EQ(-1.0 * r, residuals.at(r * 2));
|
|
EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));
|
|
}
|
|
EXPECT_EQ(420, residuals.at(20));
|
|
for (int p = 0; p < 10; ++p) {
|
|
// Check "A" Jacobian.
|
|
EXPECT_NEAR(-1.0, jacobian_vect[0][2 * p * 10 + p], kTolerance);
|
|
// Check "B" Jacobian.
|
|
EXPECT_NEAR(+1.0, jacobian_vect[0][(2 * p + 1) * 10 + p], kTolerance);
|
|
jacobian_vect[0][2 * p * 10 + p] = 0.0;
|
|
jacobian_vect[0][(2 * p + 1) * 10 + p] = 0.0;
|
|
}
|
|
|
|
// Check "C" Jacobian for first parameter block.
|
|
for (int p = 0; p < 10; ++p) {
|
|
EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance);
|
|
jacobian_vect[0][20 * 10 + p] = 0.0;
|
|
}
|
|
for (int i = 0; i < jacobian_vect[0].size(); ++i) {
|
|
EXPECT_EQ(0.0, jacobian_vect[0][i]);
|
|
}
|
|
}
|
|
|
|
// Takes 3 parameter blocks:
|
|
// parameters[0] (x) is size 1.
|
|
// parameters[1] (y) is size 2.
|
|
// parameters[2] (z) is size 3.
|
|
// Emits 7 residuals:
|
|
// A: x[0] (= sum_x)
|
|
// B: y[0] + 2.0 * y[1] (= sum_y)
|
|
// C: z[0] + 3.0 * z[1] + 6.0 * z[2] (= sum_z)
|
|
// D: sum_x * sum_y
|
|
// E: sum_y * sum_z
|
|
// F: sum_x * sum_z
|
|
// G: sum_x * sum_y * sum_z
|
|
class MyThreeParameterCostFunctor {
|
|
public:
|
|
template <typename T>
|
|
bool operator()(T const* const* parameters, T* residuals) const {
|
|
const T* x = parameters[0];
|
|
const T* y = parameters[1];
|
|
const T* z = parameters[2];
|
|
|
|
T sum_x = x[0];
|
|
T sum_y = y[0] + 2.0 * y[1];
|
|
T sum_z = z[0] + 3.0 * z[1] + 6.0 * z[2];
|
|
|
|
residuals[0] = sum_x;
|
|
residuals[1] = sum_y;
|
|
residuals[2] = sum_z;
|
|
residuals[3] = sum_x * sum_y;
|
|
residuals[4] = sum_y * sum_z;
|
|
residuals[5] = sum_x * sum_z;
|
|
residuals[6] = sum_x * sum_y * sum_z;
|
|
return true;
|
|
}
|
|
};
|
|
|
|
class ThreeParameterCostFunctorTest : public ::testing::Test {
|
|
protected:
|
|
void SetUp() final {
|
|
// Prepare the parameters.
|
|
x_.resize(1);
|
|
x_[0] = 0.0;
|
|
|
|
y_.resize(2);
|
|
y_[0] = 1.0;
|
|
y_[1] = 3.0;
|
|
|
|
z_.resize(3);
|
|
z_[0] = 2.0;
|
|
z_[1] = 4.0;
|
|
z_[2] = 6.0;
|
|
|
|
parameter_blocks_.resize(3);
|
|
parameter_blocks_[0] = &x_[0];
|
|
parameter_blocks_[1] = &y_[0];
|
|
parameter_blocks_[2] = &z_[0];
|
|
|
|
// Prepare the cost function.
|
|
typedef DynamicNumericDiffCostFunction<MyThreeParameterCostFunctor>
|
|
DynamicMyThreeParameterCostFunction;
|
|
DynamicMyThreeParameterCostFunction* cost_function =
|
|
new DynamicMyThreeParameterCostFunction(
|
|
new MyThreeParameterCostFunctor());
|
|
cost_function->AddParameterBlock(1);
|
|
cost_function->AddParameterBlock(2);
|
|
cost_function->AddParameterBlock(3);
|
|
cost_function->SetNumResiduals(7);
|
|
|
|
cost_function_.reset(cost_function);
|
|
|
|
// Setup jacobian data.
|
|
jacobian_vect_.resize(3);
|
|
jacobian_vect_[0].resize(7 * x_.size(), -100000);
|
|
jacobian_vect_[1].resize(7 * y_.size(), -100000);
|
|
jacobian_vect_[2].resize(7 * z_.size(), -100000);
|
|
|
|
// Prepare the expected residuals.
|
|
const double sum_x = x_[0];
|
|
const double sum_y = y_[0] + 2.0 * y_[1];
|
|
const double sum_z = z_[0] + 3.0 * z_[1] + 6.0 * z_[2];
|
|
|
|
expected_residuals_.resize(7);
|
|
expected_residuals_[0] = sum_x;
|
|
expected_residuals_[1] = sum_y;
|
|
expected_residuals_[2] = sum_z;
|
|
expected_residuals_[3] = sum_x * sum_y;
|
|
expected_residuals_[4] = sum_y * sum_z;
|
|
expected_residuals_[5] = sum_x * sum_z;
|
|
expected_residuals_[6] = sum_x * sum_y * sum_z;
|
|
|
|
// Prepare the expected jacobian entries.
|
|
expected_jacobian_x_.resize(7);
|
|
expected_jacobian_x_[0] = 1.0;
|
|
expected_jacobian_x_[1] = 0.0;
|
|
expected_jacobian_x_[2] = 0.0;
|
|
expected_jacobian_x_[3] = sum_y;
|
|
expected_jacobian_x_[4] = 0.0;
|
|
expected_jacobian_x_[5] = sum_z;
|
|
expected_jacobian_x_[6] = sum_y * sum_z;
|
|
|
|
expected_jacobian_y_.resize(14);
|
|
expected_jacobian_y_[0] = 0.0;
|
|
expected_jacobian_y_[1] = 0.0;
|
|
expected_jacobian_y_[2] = 1.0;
|
|
expected_jacobian_y_[3] = 2.0;
|
|
expected_jacobian_y_[4] = 0.0;
|
|
expected_jacobian_y_[5] = 0.0;
|
|
expected_jacobian_y_[6] = sum_x;
|
|
expected_jacobian_y_[7] = 2.0 * sum_x;
|
|
expected_jacobian_y_[8] = sum_z;
|
|
expected_jacobian_y_[9] = 2.0 * sum_z;
|
|
expected_jacobian_y_[10] = 0.0;
|
|
expected_jacobian_y_[11] = 0.0;
|
|
expected_jacobian_y_[12] = sum_x * sum_z;
|
|
expected_jacobian_y_[13] = 2.0 * sum_x * sum_z;
|
|
|
|
expected_jacobian_z_.resize(21);
|
|
expected_jacobian_z_[0] = 0.0;
|
|
expected_jacobian_z_[1] = 0.0;
|
|
expected_jacobian_z_[2] = 0.0;
|
|
expected_jacobian_z_[3] = 0.0;
|
|
expected_jacobian_z_[4] = 0.0;
|
|
expected_jacobian_z_[5] = 0.0;
|
|
expected_jacobian_z_[6] = 1.0;
|
|
expected_jacobian_z_[7] = 3.0;
|
|
expected_jacobian_z_[8] = 6.0;
|
|
expected_jacobian_z_[9] = 0.0;
|
|
expected_jacobian_z_[10] = 0.0;
|
|
expected_jacobian_z_[11] = 0.0;
|
|
expected_jacobian_z_[12] = sum_y;
|
|
expected_jacobian_z_[13] = 3.0 * sum_y;
|
|
expected_jacobian_z_[14] = 6.0 * sum_y;
|
|
expected_jacobian_z_[15] = sum_x;
|
|
expected_jacobian_z_[16] = 3.0 * sum_x;
|
|
expected_jacobian_z_[17] = 6.0 * sum_x;
|
|
expected_jacobian_z_[18] = sum_x * sum_y;
|
|
expected_jacobian_z_[19] = 3.0 * sum_x * sum_y;
|
|
expected_jacobian_z_[20] = 6.0 * sum_x * sum_y;
|
|
}
|
|
|
|
protected:
|
|
vector<double> x_;
|
|
vector<double> y_;
|
|
vector<double> z_;
|
|
|
|
vector<double*> parameter_blocks_;
|
|
|
|
std::unique_ptr<CostFunction> cost_function_;
|
|
|
|
vector<vector<double>> jacobian_vect_;
|
|
|
|
vector<double> expected_residuals_;
|
|
|
|
vector<double> expected_jacobian_x_;
|
|
vector<double> expected_jacobian_y_;
|
|
vector<double> expected_jacobian_z_;
|
|
};
|
|
|
|
TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterResiduals) {
|
|
vector<double> residuals(7, -100000);
|
|
EXPECT_TRUE(cost_function_->Evaluate(
|
|
parameter_blocks_.data(), residuals.data(), NULL));
|
|
for (int i = 0; i < 7; ++i) {
|
|
EXPECT_EQ(expected_residuals_[i], residuals[i]);
|
|
}
|
|
}
|
|
|
|
TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterJacobian) {
|
|
vector<double> residuals(7, -100000);
|
|
|
|
vector<double*> jacobian;
|
|
jacobian.push_back(jacobian_vect_[0].data());
|
|
jacobian.push_back(jacobian_vect_[1].data());
|
|
jacobian.push_back(jacobian_vect_[2].data());
|
|
|
|
EXPECT_TRUE(cost_function_->Evaluate(
|
|
parameter_blocks_.data(), residuals.data(), jacobian.data()));
|
|
|
|
for (int i = 0; i < 7; ++i) {
|
|
EXPECT_EQ(expected_residuals_[i], residuals[i]);
|
|
}
|
|
|
|
for (int i = 0; i < 7; ++i) {
|
|
EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance);
|
|
}
|
|
|
|
for (int i = 0; i < 14; ++i) {
|
|
EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance);
|
|
}
|
|
|
|
for (int i = 0; i < 21; ++i) {
|
|
EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance);
|
|
}
|
|
}
|
|
|
|
TEST_F(ThreeParameterCostFunctorTest,
|
|
ThreeParameterJacobianWithFirstAndLastParameterBlockConstant) {
|
|
vector<double> residuals(7, -100000);
|
|
|
|
vector<double*> jacobian;
|
|
jacobian.push_back(NULL);
|
|
jacobian.push_back(jacobian_vect_[1].data());
|
|
jacobian.push_back(NULL);
|
|
|
|
EXPECT_TRUE(cost_function_->Evaluate(
|
|
parameter_blocks_.data(), residuals.data(), jacobian.data()));
|
|
|
|
for (int i = 0; i < 7; ++i) {
|
|
EXPECT_EQ(expected_residuals_[i], residuals[i]);
|
|
}
|
|
|
|
for (int i = 0; i < 14; ++i) {
|
|
EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance);
|
|
}
|
|
}
|
|
|
|
TEST_F(ThreeParameterCostFunctorTest,
|
|
ThreeParameterJacobianWithSecondParameterBlockConstant) {
|
|
vector<double> residuals(7, -100000);
|
|
|
|
vector<double*> jacobian;
|
|
jacobian.push_back(jacobian_vect_[0].data());
|
|
jacobian.push_back(NULL);
|
|
jacobian.push_back(jacobian_vect_[2].data());
|
|
|
|
EXPECT_TRUE(cost_function_->Evaluate(
|
|
parameter_blocks_.data(), residuals.data(), jacobian.data()));
|
|
|
|
for (int i = 0; i < 7; ++i) {
|
|
EXPECT_EQ(expected_residuals_[i], residuals[i]);
|
|
}
|
|
|
|
for (int i = 0; i < 7; ++i) {
|
|
EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance);
|
|
}
|
|
|
|
for (int i = 0; i < 21; ++i) {
|
|
EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance);
|
|
}
|
|
}
|
|
|
|
} // namespace internal
|
|
} // namespace ceres
|