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clean up and formatting

release/4.3a0
Varun Agrawal 2024-10-14 21:01:31 -04:00
parent 4a0257c0ce
commit 322a23d49c
1 changed files with 29 additions and 32 deletions
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61
gtsam/nonlinear/tests/testNonlinearConjugateGradientOptimizer.cpp
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@ -1,56 +1,52 @@
/** /**
* @file NonlinearConjugateGradientOptimizer.cpp * @file testNonlinearConjugateGradientOptimizer.cpp
* @brief Test simple CG optimizer * @brief Test nonlinear CG optimizer
* @author Yong-Dian Jian * @author Yong-Dian Jian
* @author Varun Agrawal
* @date June 11, 2012 * @date June 11, 2012
*/ */
/** #include <CppUnitLite/TestHarness.h>
* @file testGradientDescentOptimizer.cpp #include <gtsam/geometry/Pose2.h>
* @brief Small test of NonlinearConjugateGradientOptimizer
* @author Yong-Dian Jian
* @date Jun 11, 2012
*/
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h> #include <gtsam/nonlinear/NonlinearConjugateGradientOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h> #include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h> #include <gtsam/nonlinear/Values.h>
#include <gtsam/geometry/Pose2.h> #include <gtsam/slam/BetweenFactor.h>
#include <CppUnitLite/TestHarness.h>
using namespace std; using namespace std;
using namespace gtsam; using namespace gtsam;
// Generate a small PoseSLAM problem // Generate a small PoseSLAM problem
std::tuple<NonlinearFactorGraph, Values> generateProblem() { std::tuple<NonlinearFactorGraph, Values> generateProblem() {
// 1. Create graph container and add factors to it // 1. Create graph container and add factors to it
NonlinearFactorGraph graph; NonlinearFactorGraph graph;
// 2a. Add Gaussian prior // 2a. Add Gaussian prior
Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas( SharedDiagonal priorNoise =
Vector3(0.3, 0.3, 0.1)); noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
graph.addPrior(1, priorMean, priorNoise); graph.addPrior(1, priorMean, priorNoise);
// 2b. Add odometry factors // 2b. Add odometry factors
SharedDiagonal odometryNoise = noiseModel::Diagonal::Sigmas( SharedDiagonal odometryNoise =
Vector3(0.2, 0.2, 0.1)); noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise); graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, Pose2(2.0, 0.0, 0.0),
graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise); odometryNoise);
graph.emplace_shared<BetweenFactor<Pose2>>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise); graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, Pose2(2.0, 0.0, M_PI_2),
graph.emplace_shared<BetweenFactor<Pose2>>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise); odometryNoise);
graph.emplace_shared<BetweenFactor<Pose2>>(3, 4, Pose2(2.0, 0.0, M_PI_2),
odometryNoise);
graph.emplace_shared<BetweenFactor<Pose2>>(4, 5, Pose2(2.0, 0.0, M_PI_2),
odometryNoise);
// 2c. Add pose constraint // 2c. Add pose constraint
SharedDiagonal constraintUncertainty = noiseModel::Diagonal::Sigmas( SharedDiagonal constraintUncertainty =
Vector3(0.2, 0.2, 0.1)); noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
graph.emplace_shared<BetweenFactor<Pose2>>(5, 2, Pose2(2.0, 0.0, M_PI_2), graph.emplace_shared<BetweenFactor<Pose2>>(5, 2, Pose2(2.0, 0.0, M_PI_2),
constraintUncertainty); constraintUncertainty);
// 3. Create the data structure to hold the initialEstimate estimate to the solution // 3. Create the data structure to hold the initialEstimate estimate to the
// solution
Values initialEstimate; Values initialEstimate;
Pose2 x1(0.5, 0.0, 0.2); Pose2 x1(0.5, 0.0, 0.2);
initialEstimate.insert(1, x1); initialEstimate.insert(1, x1);
@ -68,16 +64,17 @@ std::tuple<NonlinearFactorGraph, Values> generateProblem() {
/* ************************************************************************* */ /* ************************************************************************* */
TEST(NonlinearConjugateGradientOptimizer, Optimize) { TEST(NonlinearConjugateGradientOptimizer, Optimize) {
const auto [graph, initialEstimate] = generateProblem(); const auto [graph, initialEstimate] = generateProblem();
// cout << "initial error = " << graph.error(initialEstimate) << endl; // cout << "initial error = " << graph.error(initialEstimate) << endl;
NonlinearOptimizerParams param; NonlinearOptimizerParams param;
param.maxIterations = 500; /* requires a larger number of iterations to converge */ param.maxIterations =
500; /* requires a larger number of iterations to converge */
param.verbosity = NonlinearOptimizerParams::SILENT; param.verbosity = NonlinearOptimizerParams::SILENT;
NonlinearConjugateGradientOptimizer optimizer(graph, initialEstimate, param); NonlinearConjugateGradientOptimizer optimizer(graph, initialEstimate, param);
Values result = optimizer.optimize(); Values result = optimizer.optimize();
// cout << "cg final error = " << graph.error(result) << endl; // cout << "cg final error = " << graph.error(result) << endl;
EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4); EXPECT_DOUBLES_EQUAL(0.0, graph.error(result), 1e-4);
} }