mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Sameer Agarwal
329 lines
13 KiB
C++
329 lines
13 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2022 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 <limits>
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#include <memory>
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#include "ceres/dynamic_numeric_diff_cost_function.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/manifold.h"
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#include "ceres/numeric_diff_options.h"
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#include "ceres/types.h"
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#include "gmock/gmock.h"
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#include "gtest/gtest.h"
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namespace ceres {
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// Matchers and macros for help with testing Manifold objects.
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//
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// Testing a Manifold has two parts.
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//
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// 1. Checking that Manifold::Plus is correctly defined. This requires per
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// manifold tests.
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//
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// 2. The other methods of the manifold have mathematical properties that make
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// it compatible with Plus, as described in:
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//
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// "Integrating Generic Sensor Fusion Algorithms with Sound State
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// Representations through Encapsulation of Manifolds"
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// By C. Hertzberg, R. Wagner, U. Frese and L. Schroder
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// https://arxiv.org/pdf/1107.1119.pdf
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//
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// These tests are implemented using generic matchers defined below which can
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// all be called by the macro EXPECT_THAT_MANIFOLD_INVARIANTS_HOLD(manifold, x,
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// delta, y, tolerance). See manifold_test.cc for example usage.
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// Checks that the invariant Plus(x, 0) == x holds.
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MATCHER_P2(XPlusZeroIsXAt, x, tolerance, "") {
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const int ambient_size = arg.AmbientSize();
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const int tangent_size = arg.TangentSize();
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Vector actual = Vector::Zero(ambient_size);
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Vector zero = Vector::Zero(tangent_size);
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EXPECT_TRUE(arg.Plus(x.data(), zero.data(), actual.data()));
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const double n = (actual - x).norm();
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const double d = x.norm();
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const double diffnorm = (d == 0.0) ? n : (n / d);
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if (diffnorm > tolerance) {
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*result_listener << "\nexpected (x): " << x.transpose()
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<< "\nactual: " << actual.transpose()
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<< "\ndiffnorm: " << diffnorm;
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return false;
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}
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return true;
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}
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// Checks that the invariant Minus(x, x) == 0 holds.
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MATCHER_P2(XMinusXIsZeroAt, x, tolerance, "") {
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const int tangent_size = arg.TangentSize();
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Vector actual = Vector::Zero(tangent_size);
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EXPECT_TRUE(arg.Minus(x.data(), x.data(), actual.data()));
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const double diffnorm = actual.norm();
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if (diffnorm > tolerance) {
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*result_listener << "\nx: " << x.transpose() //
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<< "\nexpected: 0 0 0"
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<< "\nactual: " << actual.transpose()
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<< "\ndiffnorm: " << diffnorm;
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return false;
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}
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return true;
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}
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// Helper struct to curry Plus(x, .) so that it can be numerically
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// differentiated.
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struct PlusFunctor {
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PlusFunctor(const Manifold& manifold, const double* x)
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: manifold(manifold), x(x) {}
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bool operator()(double const* const* parameters, double* x_plus_delta) const {
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return manifold.Plus(x, parameters[0], x_plus_delta);
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}
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const Manifold& manifold;
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const double* x;
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};
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// Checks that the output of PlusJacobian matches the one obtained by
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// numerically evaluating D_2 Plus(x,0).
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MATCHER_P2(HasCorrectPlusJacobianAt, x, tolerance, "") {
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const int ambient_size = arg.AmbientSize();
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const int tangent_size = arg.TangentSize();
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NumericDiffOptions options;
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options.ridders_relative_initial_step_size = 1e-4;
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DynamicNumericDiffCostFunction<PlusFunctor, RIDDERS> cost_function(
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new PlusFunctor(arg, x.data()), TAKE_OWNERSHIP, options);
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cost_function.AddParameterBlock(tangent_size);
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cost_function.SetNumResiduals(ambient_size);
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Vector zero = Vector::Zero(tangent_size);
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double* parameters[1] = {zero.data()};
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Vector x_plus_zero = Vector::Zero(ambient_size);
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Matrix expected = Matrix::Zero(ambient_size, tangent_size);
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double* jacobians[1] = {expected.data()};
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EXPECT_TRUE(
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cost_function.Evaluate(parameters, x_plus_zero.data(), jacobians));
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Matrix actual = Matrix::Random(ambient_size, tangent_size);
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EXPECT_TRUE(arg.PlusJacobian(x.data(), actual.data()));
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const double n = (actual - expected).norm();
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const double d = expected.norm();
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const double diffnorm = (d == 0.0) ? n : n / d;
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if (diffnorm > tolerance) {
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*result_listener << "\nx: " << x.transpose() << "\nexpected: \n"
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<< expected << "\nactual:\n"
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<< actual << "\ndiff:\n"
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<< expected - actual << "\ndiffnorm : " << diffnorm;
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return false;
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}
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return true;
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}
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// Checks that the invariant Minus(Plus(x, delta), x) == delta holds.
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MATCHER_P3(MinusPlusIsIdentityAt, x, delta, tolerance, "") {
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const int ambient_size = arg.AmbientSize();
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const int tangent_size = arg.TangentSize();
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Vector x_plus_delta = Vector::Zero(ambient_size);
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EXPECT_TRUE(arg.Plus(x.data(), delta.data(), x_plus_delta.data()));
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Vector actual = Vector::Zero(tangent_size);
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EXPECT_TRUE(arg.Minus(x_plus_delta.data(), x.data(), actual.data()));
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const double n = (actual - delta).norm();
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const double d = delta.norm();
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const double diffnorm = (d == 0.0) ? n : (n / d);
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if (diffnorm > tolerance) {
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*result_listener << "\nx: " << x.transpose()
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<< "\nexpected: " << delta.transpose()
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<< "\nactual:" << actual.transpose()
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<< "\ndiff:" << (delta - actual).transpose()
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<< "\ndiffnorm: " << diffnorm;
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return false;
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}
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return true;
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}
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// Checks that the invariant Plus(Minus(y, x), x) == y holds.
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MATCHER_P3(PlusMinusIsIdentityAt, x, y, tolerance, "") {
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const int ambient_size = arg.AmbientSize();
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const int tangent_size = arg.TangentSize();
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Vector y_minus_x = Vector::Zero(tangent_size);
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EXPECT_TRUE(arg.Minus(y.data(), x.data(), y_minus_x.data()));
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Vector actual = Vector::Zero(ambient_size);
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EXPECT_TRUE(arg.Plus(x.data(), y_minus_x.data(), actual.data()));
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const double n = (actual - y).norm();
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const double d = y.norm();
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const double diffnorm = (d == 0.0) ? n : (n / d);
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if (diffnorm > tolerance) {
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*result_listener << "\nx: " << x.transpose()
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<< "\nexpected: " << y.transpose()
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<< "\nactual:" << actual.transpose()
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<< "\ndiff:" << (y - actual).transpose()
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<< "\ndiffnorm: " << diffnorm;
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return false;
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}
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return true;
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}
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// Helper struct to curry Minus(., x) so that it can be numerically
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// differentiated.
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struct MinusFunctor {
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MinusFunctor(const Manifold& manifold, const double* x)
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: manifold(manifold), x(x) {}
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bool operator()(double const* const* parameters, double* y_minus_x) const {
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return manifold.Minus(parameters[0], x, y_minus_x);
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}
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const Manifold& manifold;
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const double* x;
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};
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// Checks that the output of MinusJacobian matches the one obtained by
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// numerically evaluating D_1 Minus(x,x).
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MATCHER_P2(HasCorrectMinusJacobianAt, x, tolerance, "") {
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const int ambient_size = arg.AmbientSize();
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const int tangent_size = arg.TangentSize();
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Vector y = x;
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Vector y_minus_x = Vector::Zero(tangent_size);
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NumericDiffOptions options;
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options.ridders_relative_initial_step_size = 1e-4;
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DynamicNumericDiffCostFunction<MinusFunctor, RIDDERS> cost_function(
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new MinusFunctor(arg, x.data()), TAKE_OWNERSHIP, options);
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cost_function.AddParameterBlock(ambient_size);
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cost_function.SetNumResiduals(tangent_size);
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double* parameters[1] = {y.data()};
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Matrix expected = Matrix::Zero(tangent_size, ambient_size);
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double* jacobians[1] = {expected.data()};
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EXPECT_TRUE(cost_function.Evaluate(parameters, y_minus_x.data(), jacobians));
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Matrix actual = Matrix::Random(tangent_size, ambient_size);
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EXPECT_TRUE(arg.MinusJacobian(x.data(), actual.data()));
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const double n = (actual - expected).norm();
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const double d = expected.norm();
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const double diffnorm = (d == 0.0) ? n : (n / d);
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if (diffnorm > tolerance) {
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*result_listener << "\nx: " << x.transpose() << "\nexpected: \n"
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<< expected << "\nactual:\n"
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<< actual << "\ndiff:\n"
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<< expected - actual << "\ndiffnorm: " << diffnorm;
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return false;
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}
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return true;
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}
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// Checks that D_delta Minus(Plus(x, delta), x) at delta = 0 is an identity
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// matrix.
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MATCHER_P2(MinusPlusJacobianIsIdentityAt, x, tolerance, "") {
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const int ambient_size = arg.AmbientSize();
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const int tangent_size = arg.TangentSize();
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Matrix plus_jacobian(ambient_size, tangent_size);
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EXPECT_TRUE(arg.PlusJacobian(x.data(), plus_jacobian.data()));
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Matrix minus_jacobian(tangent_size, ambient_size);
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EXPECT_TRUE(arg.MinusJacobian(x.data(), minus_jacobian.data()));
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const Matrix actual = minus_jacobian * plus_jacobian;
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const Matrix expected = Matrix::Identity(tangent_size, tangent_size);
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const double n = (actual - expected).norm();
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const double d = expected.norm();
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const double diffnorm = n / d;
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if (diffnorm > tolerance) {
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*result_listener << "\nx: " << x.transpose() << "\nexpected: \n"
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<< expected << "\nactual:\n"
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<< actual << "\ndiff:\n"
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<< expected - actual << "\ndiffnorm: " << diffnorm;
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return false;
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}
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return true;
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}
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// Verify that the output of RightMultiplyByPlusJacobian is ambient_matrix *
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// plus_jacobian.
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MATCHER_P2(HasCorrectRightMultiplyByPlusJacobianAt, x, tolerance, "") {
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const int ambient_size = arg.AmbientSize();
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const int tangent_size = arg.TangentSize();
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constexpr int kMinNumRows = 0;
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constexpr int kMaxNumRows = 3;
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for (int num_rows = kMinNumRows; num_rows <= kMaxNumRows; ++num_rows) {
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Matrix plus_jacobian = Matrix::Random(ambient_size, tangent_size);
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EXPECT_TRUE(arg.PlusJacobian(x.data(), plus_jacobian.data()));
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Matrix ambient_matrix = Matrix::Random(num_rows, ambient_size);
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Matrix expected = ambient_matrix * plus_jacobian;
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Matrix actual = Matrix::Random(num_rows, tangent_size);
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EXPECT_TRUE(arg.RightMultiplyByPlusJacobian(
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x.data(), num_rows, ambient_matrix.data(), actual.data()));
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const double n = (actual - expected).norm();
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const double d = expected.norm();
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const double diffnorm = (d == 0.0) ? n : (n / d);
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if (diffnorm > tolerance) {
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*result_listener << "\nx: " << x.transpose() << "\nambient_matrix : \n"
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<< ambient_matrix << "\nplus_jacobian : \n"
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<< plus_jacobian << "\nexpected: \n"
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<< expected << "\nactual:\n"
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<< actual << "\ndiff:\n"
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<< expected - actual << "\ndiffnorm : " << diffnorm;
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return false;
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}
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}
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return true;
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}
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#define EXPECT_THAT_MANIFOLD_INVARIANTS_HOLD(manifold, x, delta, y, tolerance) \
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Vector zero_tangent = Vector::Zero(manifold.TangentSize()); \
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EXPECT_THAT(manifold, XPlusZeroIsXAt(x, tolerance)); \
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EXPECT_THAT(manifold, XMinusXIsZeroAt(x, tolerance)); \
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EXPECT_THAT(manifold, MinusPlusIsIdentityAt(x, delta, tolerance)); \
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EXPECT_THAT(manifold, MinusPlusIsIdentityAt(x, zero_tangent, tolerance)); \
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EXPECT_THAT(manifold, PlusMinusIsIdentityAt(x, x, tolerance)); \
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EXPECT_THAT(manifold, PlusMinusIsIdentityAt(x, y, tolerance)); \
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EXPECT_THAT(manifold, HasCorrectPlusJacobianAt(x, tolerance)); \
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EXPECT_THAT(manifold, HasCorrectMinusJacobianAt(x, tolerance)); \
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EXPECT_THAT(manifold, MinusPlusJacobianIsIdentityAt(x, tolerance)); \
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EXPECT_THAT(manifold, HasCorrectRightMultiplyByPlusJacobianAt(x, tolerance));
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} // namespace ceres
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