601 lines
24 KiB
C++
601 lines
24 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testConcurrentIncrementalSmoother.cpp
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* @brief Unit tests for the Concurrent Batch Smoother
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* @author Stephen Williams (swilliams8@gatech.edu)
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* @date Jan 5, 2013
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*/
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#include <gtsam_unstable/nonlinear/ConcurrentIncrementalSmoother.h>
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#include <gtsam/nonlinear/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/LinearContainerFactor.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/inference/Key.h>
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#include <gtsam/inference/Ordering.h>
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#include <gtsam/inference/JunctionTree.h>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <CppUnitLite/TestHarness.h>
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using namespace std;
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using namespace gtsam;
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namespace {
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// Set up initial pose, odometry difference, loop closure difference, and initialization errors
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const Pose3 poseInitial;
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const Pose3 poseOdometry( Rot3::RzRyRx(Vector3(0.05, 0.10, -0.75)), Point3(1.0, -0.25, 0.10) );
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const Pose3 poseError( Rot3::RzRyRx(Vector3(0.01, 0.02, -0.1)), Point3(0.05, -0.05, 0.02) );
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// Set up noise models for the factors
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const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
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const SharedDiagonal noiseOdometery = noiseModel::Diagonal::Sigmas((Vector(6) << 0.1, 0.1, 0.1, 0.5, 0.5, 0.5).finished());
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const SharedDiagonal noiseLoop = noiseModel::Diagonal::Sigmas((Vector(6) << 0.25, 0.25, 0.25, 1.0, 1.0, 1.0).finished());
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/* ************************************************************************* */
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Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int maxIter = 100) {
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// Create an L-M optimizer
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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// parameters.maxIterations = maxIter;
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// parameters.lambdaUpperBound = 1;
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// parameters.lambdaInitial = 1;
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// parameters.verbosity = NonlinearOptimizerParams::ERROR;
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// parameters.verbosityLM = ISAM2Params::DAMPED;
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// parameters.linearSolverType = NonlinearOptimizerParams::MULTIFRONTAL_QR;
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ISAM2 optimizer(parameters);
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optimizer.update( graph, theta );
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Values result = optimizer.calculateEstimate();
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return result;
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}
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} // end namespace
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, equals )
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{
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// TODO: Test 'equals' more vigorously
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// Create a Concurrent Batch Smoother
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ISAM2Params parameters1;
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parameters1.optimizationParams = ISAM2DoglegParams();
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ConcurrentIncrementalSmoother smoother1(parameters1);
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// Create an identical Concurrent Batch Smoother
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ISAM2Params parameters2;
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parameters2.optimizationParams = ISAM2DoglegParams();
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ConcurrentIncrementalSmoother smoother2(parameters2);
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// Create a different Concurrent Batch Smoother
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ISAM2Params parameters3;
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parameters3.optimizationParams = ISAM2DoglegParams();
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// parameters3.maxIterations = 1;
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ConcurrentIncrementalSmoother smoother3(parameters3);
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CHECK(assert_equal(smoother1, smoother1));
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CHECK(assert_equal(smoother1, smoother2));
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// CHECK(assert_inequal(smoother1, smoother3));
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, getFactors )
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{
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// Create a Concurrent Batch Smoother
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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ConcurrentIncrementalSmoother smoother(parameters);
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// Expected graph is empty
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NonlinearFactorGraph expected1;
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// Get actual graph
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NonlinearFactorGraph actual1 = smoother.getFactors();
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// Check
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CHECK(assert_equal(expected1, actual1));
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// Add some factors to the smoother
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NonlinearFactorGraph newFactors1;
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newFactors1.addPrior(1, poseInitial, noisePrior);
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newFactors1.push_back(BetweenFactor<Pose3>(1, 2, poseOdometry, noiseOdometery));
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Values newValues1;
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newValues1.insert(1, Pose3());
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newValues1.insert(2, newValues1.at<Pose3>(1).compose(poseOdometry));
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smoother.update(newFactors1, newValues1);
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// Expected graph
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NonlinearFactorGraph expected2;
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expected2.push_back(newFactors1);
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// Get actual graph
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NonlinearFactorGraph actual2 = smoother.getFactors();
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// Check
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CHECK(assert_equal(expected2, actual2));
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// Add some more factors to the smoother
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NonlinearFactorGraph newFactors2;
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newFactors2.push_back(BetweenFactor<Pose3>(2, 3, poseOdometry, noiseOdometery));
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newFactors2.push_back(BetweenFactor<Pose3>(3, 4, poseOdometry, noiseOdometery));
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Values newValues2;
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newValues2.insert(3, newValues1.at<Pose3>(2).compose(poseOdometry));
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newValues2.insert(4, newValues2.at<Pose3>(3).compose(poseOdometry));
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smoother.update(newFactors2, newValues2);
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// Expected graph
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NonlinearFactorGraph expected3;
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expected3.push_back(newFactors1);
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expected3.push_back(newFactors2);
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// Get actual graph
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NonlinearFactorGraph actual3 = smoother.getFactors();
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// Check
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CHECK(assert_equal(expected3, actual3));
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, getLinearizationPoint )
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{
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// Create a Concurrent Batch Smoother
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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ConcurrentIncrementalSmoother smoother(parameters);
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// Expected values is empty
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Values expected1;
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// Get Linearization Point
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Values actual1 = smoother.getLinearizationPoint();
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// Check
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CHECK(assert_equal(expected1, actual1));
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// Add some factors to the smoother
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NonlinearFactorGraph newFactors1;
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newFactors1.addPrior(1, poseInitial, noisePrior);
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newFactors1.push_back(BetweenFactor<Pose3>(1, 2, poseOdometry, noiseOdometery));
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Values newValues1;
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newValues1.insert(1, Pose3());
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newValues1.insert(2, newValues1.at<Pose3>(1).compose(poseOdometry));
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smoother.update(newFactors1, newValues1);
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// Expected values is equivalent to the provided values only because the provided linearization points were optimal
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Values expected2;
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expected2.insert(newValues1);
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// Get actual linearization point
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Values actual2 = smoother.getLinearizationPoint();
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// Check
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CHECK(assert_equal(expected2, actual2));
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// Add some more factors to the smoother
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NonlinearFactorGraph newFactors2;
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newFactors2.push_back(BetweenFactor<Pose3>(2, 3, poseOdometry, noiseOdometery));
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newFactors2.push_back(BetweenFactor<Pose3>(3, 4, poseOdometry, noiseOdometery));
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Values newValues2;
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newValues2.insert(3, newValues1.at<Pose3>(2).compose(poseOdometry));
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newValues2.insert(4, newValues2.at<Pose3>(3).compose(poseOdometry));
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smoother.update(newFactors2, newValues2);
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// Expected values is equivalent to the provided values only because the provided linearization points were optimal
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Values expected3;
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expected3.insert(newValues1);
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expected3.insert(newValues2);
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// Get actual linearization point
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Values actual3 = smoother.getLinearizationPoint();
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// Check
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CHECK(assert_equal(expected3, actual3));
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, getDelta )
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{
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// TODO: Think about how to check ordering...
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, calculateEstimate )
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{
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// Create a Concurrent Batch Smoother
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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ConcurrentIncrementalSmoother smoother(parameters);
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// Expected values is empty
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Values expected1;
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// Get Linearization Point
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Values actual1 = smoother.calculateEstimate();
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// Check
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CHECK(assert_equal(expected1, actual1));
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// Add some factors to the smoother
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NonlinearFactorGraph newFactors2;
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newFactors2.addPrior(1, poseInitial, noisePrior);
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newFactors2.push_back(BetweenFactor<Pose3>(1, 2, poseOdometry, noiseOdometery));
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Values newValues2;
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newValues2.insert(1, Pose3().compose(poseError));
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newValues2.insert(2, newValues2.at<Pose3>(1).compose(poseOdometry).compose(poseError));
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smoother.update(newFactors2, newValues2);
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// Expected values from full batch optimization
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NonlinearFactorGraph allFactors2;
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allFactors2.push_back(newFactors2);
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Values allValues2;
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allValues2.insert(newValues2);
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Values expected2 = BatchOptimize(allFactors2, allValues2);
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// Get actual linearization point
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Values actual2 = smoother.calculateEstimate();
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// Check
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CHECK(assert_equal(expected2, actual2, 1e-6));
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// Add some more factors to the smoother
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NonlinearFactorGraph newFactors3;
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newFactors3.push_back(BetweenFactor<Pose3>(2, 3, poseOdometry, noiseOdometery));
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newFactors3.push_back(BetweenFactor<Pose3>(3, 4, poseOdometry, noiseOdometery));
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Values newValues3;
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newValues3.insert(3, newValues2.at<Pose3>(2).compose(poseOdometry).compose(poseError));
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newValues3.insert(4, newValues3.at<Pose3>(3).compose(poseOdometry).compose(poseError));
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smoother.update(newFactors3, newValues3);
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// Expected values from full batch optimization
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NonlinearFactorGraph allFactors3;
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allFactors3.push_back(newFactors2);
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allFactors3.push_back(newFactors3);
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Values allValues3;
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allValues3.insert(newValues2);
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allValues3.insert(newValues3);
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Values expected3 = BatchOptimize(allFactors3, allValues3);
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// Get actual linearization point
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Values actual3 = smoother.calculateEstimate();
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// Check
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CHECK(assert_equal(expected3, actual3, 1e-6));
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// Also check the single-variable version
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Pose3 expectedPose1 = expected3.at<Pose3>(1);
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Pose3 expectedPose2 = expected3.at<Pose3>(2);
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Pose3 expectedPose3 = expected3.at<Pose3>(3);
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Pose3 expectedPose4 = expected3.at<Pose3>(4);
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// Also check the single-variable version
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Pose3 actualPose1 = smoother.calculateEstimate<Pose3>(1);
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Pose3 actualPose2 = smoother.calculateEstimate<Pose3>(2);
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Pose3 actualPose3 = smoother.calculateEstimate<Pose3>(3);
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Pose3 actualPose4 = smoother.calculateEstimate<Pose3>(4);
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// Check
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CHECK(assert_equal(expectedPose1, actualPose1, 1e-6));
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CHECK(assert_equal(expectedPose2, actualPose2, 1e-6));
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CHECK(assert_equal(expectedPose3, actualPose3, 1e-6));
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CHECK(assert_equal(expectedPose4, actualPose4, 1e-6));
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, update_empty )
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{
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// Create a set of optimizer parameters
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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// Create a Concurrent Batch Smoother
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ConcurrentIncrementalSmoother smoother(parameters);
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// Call update
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smoother.update();
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, update_multiple )
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{
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// Create a Concurrent Batch Smoother
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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ConcurrentIncrementalSmoother smoother(parameters);
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// Expected values is empty
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Values expected1;
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// Get Linearization Point
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Values actual1 = smoother.calculateEstimate();
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// Check
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CHECK(assert_equal(expected1, actual1));
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// Add some factors to the smoother
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NonlinearFactorGraph newFactors2;
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newFactors2.addPrior(1, poseInitial, noisePrior);
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newFactors2.push_back(BetweenFactor<Pose3>(1, 2, poseOdometry, noiseOdometery));
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Values newValues2;
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newValues2.insert(1, Pose3().compose(poseError));
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newValues2.insert(2, newValues2.at<Pose3>(1).compose(poseOdometry).compose(poseError));
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smoother.update(newFactors2, newValues2);
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// Expected values from full batch optimization
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NonlinearFactorGraph allFactors2;
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allFactors2.push_back(newFactors2);
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Values allValues2;
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allValues2.insert(newValues2);
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Values expected2 = BatchOptimize(allFactors2, allValues2);
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// Get actual linearization point
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Values actual2 = smoother.calculateEstimate();
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// Check
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CHECK(assert_equal(expected2, actual2, 1e-6));
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// Add some more factors to the smoother
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NonlinearFactorGraph newFactors3;
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newFactors3.push_back(BetweenFactor<Pose3>(2, 3, poseOdometry, noiseOdometery));
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newFactors3.push_back(BetweenFactor<Pose3>(3, 4, poseOdometry, noiseOdometery));
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Values newValues3;
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newValues3.insert(3, newValues2.at<Pose3>(2).compose(poseOdometry).compose(poseError));
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newValues3.insert(4, newValues3.at<Pose3>(3).compose(poseOdometry).compose(poseError));
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smoother.update(newFactors3, newValues3);
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// Expected values from full batch optimization
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NonlinearFactorGraph allFactors3;
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allFactors3.push_back(newFactors2);
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allFactors3.push_back(newFactors3);
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Values allValues3;
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allValues3.insert(newValues2);
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allValues3.insert(newValues3);
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Values expected3 = BatchOptimize(allFactors3, allValues3);
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// Get actual linearization point
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Values actual3 = smoother.calculateEstimate();
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// Check
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CHECK(assert_equal(expected3, actual3, 1e-6));
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, synchronize_empty )
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{
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// Create a set of optimizer parameters
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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// Create a Concurrent Batch Smoother
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ConcurrentIncrementalSmoother smoother(parameters);
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// Create empty containers *from* the filter
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NonlinearFactorGraph smootherFactors, filterSumarization;
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Values smootherValues, filterSeparatorValues;
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// Create expected values: these will be empty for this case
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NonlinearFactorGraph expectedSmootherSummarization;
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Values expectedSmootherSeparatorValues;
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// Synchronize
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NonlinearFactorGraph actualSmootherSummarization;
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Values actualSmootherSeparatorValues;
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smoother.presync();
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smoother.getSummarizedFactors(actualSmootherSummarization, actualSmootherSeparatorValues);
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smoother.synchronize(smootherFactors, smootherValues, filterSumarization, filterSeparatorValues);
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smoother.postsync();
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// Check
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CHECK(assert_equal(expectedSmootherSummarization, actualSmootherSummarization, 1e-6));
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CHECK(assert_equal(expectedSmootherSeparatorValues, actualSmootherSeparatorValues, 1e-6));
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, synchronize_1 )
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{
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// Create a set of optimizer parameters
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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// parameters.maxIterations = 1;
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// Create a Concurrent Batch Smoother
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ConcurrentIncrementalSmoother smoother(parameters);
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// Create a simple separator *from* the filter
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NonlinearFactorGraph smootherFactors, filterSumarization;
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Values smootherValues, filterSeparatorValues;
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filterSeparatorValues.insert(1, Pose3().compose(poseError));
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filterSeparatorValues.insert(2, filterSeparatorValues.at<Pose3>(1).compose(poseOdometry).compose(poseError));
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filterSumarization.push_back(LinearContainerFactor(PriorFactor<Pose3>(1, poseInitial, noisePrior).linearize(filterSeparatorValues), filterSeparatorValues));
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filterSumarization.push_back(LinearContainerFactor(BetweenFactor<Pose3>(1, 2, poseOdometry, noiseOdometery).linearize(filterSeparatorValues), filterSeparatorValues));
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// Create expected values: the smoother output will be empty for this case
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NonlinearFactorGraph expectedSmootherSummarization;
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Values expectedSmootherSeparatorValues;
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NonlinearFactorGraph actualSmootherSummarization;
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Values actualSmootherSeparatorValues;
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smoother.presync();
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smoother.getSummarizedFactors(actualSmootherSummarization, actualSmootherSeparatorValues);
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smoother.synchronize(smootherFactors, smootherValues, filterSumarization, filterSeparatorValues);
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smoother.postsync();
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// Check
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CHECK(assert_equal(expectedSmootherSummarization, actualSmootherSummarization, 1e-6));
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CHECK(assert_equal(expectedSmootherSeparatorValues, actualSmootherSeparatorValues, 1e-6));
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// Update the smoother
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smoother.update();
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// Check the factor graph. It should contain only the filter-provided factors
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NonlinearFactorGraph expectedFactorGraph = filterSumarization;
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NonlinearFactorGraph actualFactorGraph = smoother.getFactors();
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CHECK(assert_equal(expectedFactorGraph, actualFactorGraph, 1e-6));
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// Check the optimized value of smoother state
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NonlinearFactorGraph allFactors;
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allFactors.push_back(filterSumarization);
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Values allValues;
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allValues.insert(filterSeparatorValues);
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Values expectedValues = BatchOptimize(allFactors, allValues,1);
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Values actualValues = smoother.calculateEstimate();
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CHECK(assert_equal(expectedValues, actualValues, 1e-6));
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// Check the linearization point. The separator should remain identical to the filter provided values
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Values expectedLinearizationPoint = filterSeparatorValues;
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Values actualLinearizationPoint = smoother.getLinearizationPoint();
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CHECK(assert_equal(expectedLinearizationPoint, actualLinearizationPoint, 1e-6));
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, synchronize_2 )
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{
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// Create a set of optimizer parameters
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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// parameters.maxIterations = 1;
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// parameters.lambdaUpperBound = 1;
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// parameters.lambdaInitial = 1;
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// parameters.verbosity = NonlinearOptimizerParams::ERROR;
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// parameters.verbosityLM = ISAM2Params::DAMPED;
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// Create a Concurrent Batch Smoother
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ConcurrentIncrementalSmoother smoother(parameters);
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// Create a separator and cached smoother factors *from* the filter
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NonlinearFactorGraph smootherFactors, filterSumarization;
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Values smootherValues, filterSeparatorValues;
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filterSeparatorValues.insert(1, Pose3().compose(poseError));
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filterSeparatorValues.insert(2, filterSeparatorValues.at<Pose3>(1).compose(poseOdometry).compose(poseError));
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filterSumarization.push_back(LinearContainerFactor(PriorFactor<Pose3>(1, poseInitial, noisePrior).linearize(filterSeparatorValues), filterSeparatorValues));
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filterSumarization.push_back(LinearContainerFactor(BetweenFactor<Pose3>(1, 2, poseOdometry, noiseOdometery).linearize(filterSeparatorValues), filterSeparatorValues));
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smootherFactors.push_back(BetweenFactor<Pose3>(2, 3, poseOdometry, noiseOdometery));
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smootherFactors.push_back(BetweenFactor<Pose3>(3, 4, poseOdometry, noiseOdometery));
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smootherValues.insert(3, filterSeparatorValues.at<Pose3>(2).compose(poseOdometry).compose(poseError));
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smootherValues.insert(4, smootherValues.at<Pose3>(3).compose(poseOdometry).compose(poseError));
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// Create expected values: the smoother output will be empty for this case
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NonlinearFactorGraph expectedSmootherSummarization;
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Values expectedSmootherSeparatorValues;
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NonlinearFactorGraph actualSmootherSummarization;
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Values actualSmootherSeparatorValues;
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smoother.presync();
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smoother.getSummarizedFactors(actualSmootherSummarization, actualSmootherSeparatorValues);
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smoother.synchronize(smootherFactors, smootherValues, filterSumarization, filterSeparatorValues);
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smoother.postsync();
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// Check
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CHECK(assert_equal(expectedSmootherSummarization, actualSmootherSummarization, 1e-6));
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CHECK(assert_equal(expectedSmootherSeparatorValues, actualSmootherSeparatorValues, 1e-6));
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// Update the smoother
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smoother.update();
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// Check the factor graph. It should contain only the filter-provided factors
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NonlinearFactorGraph expectedFactorGraph;
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expectedFactorGraph.push_back(smootherFactors);
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expectedFactorGraph.push_back(filterSumarization);
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NonlinearFactorGraph actualFactorGraph = smoother.getFactors();
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CHECK(assert_equal(expectedFactorGraph, actualFactorGraph, 1e-6));
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// Check the optimized value of smoother state
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NonlinearFactorGraph allFactors;
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allFactors.push_back(filterSumarization);
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allFactors.push_back(smootherFactors);
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Values allValues;
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allValues.insert(filterSeparatorValues);
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allValues.insert(smootherValues);
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// allValues.print("Batch LinPoint:\n");
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Values expectedValues = BatchOptimize(allFactors, allValues, 1);
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Values actualValues = smoother.calculateEstimate();
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CHECK(assert_equal(expectedValues, actualValues, 1e-6));
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// Check the linearization point. The separator should remain identical to the filter provided values, but the others should be the optimal values
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// Isam2 is changing internally the linearization points, so the following check is done only on the separator variables
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// Values expectedLinearizationPoint = BatchOptimize(allFactors, allValues, 1);
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Values expectedLinearizationPoint = filterSeparatorValues;
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Values actualLinearizationPoint;
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for(const auto key_value: filterSeparatorValues) {
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actualLinearizationPoint.insert(key_value.key, smoother.getLinearizationPoint().at(key_value.key));
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}
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CHECK(assert_equal(expectedLinearizationPoint, actualLinearizationPoint, 1e-6));
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}
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/* ************************************************************************* */
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TEST( ConcurrentIncrementalSmootherDL, synchronize_3 )
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{
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// Create a set of optimizer parameters
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ISAM2Params parameters;
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parameters.optimizationParams = ISAM2DoglegParams();
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// parameters.maxIterations = 1;
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// parameters.lambdaUpperBound = 1;
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// parameters.lambdaInitial = 1;
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// parameters.verbosity = NonlinearOptimizerParams::ERROR;
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// parameters.verbosityLM = ISAM2Params::DAMPED;
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|
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// Create a Concurrent Batch Smoother
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ConcurrentIncrementalSmoother smoother(parameters);
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|
|
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// Create a separator and cached smoother factors *from* the filter
|
|
NonlinearFactorGraph smootherFactors, filterSumarization;
|
|
Values smootherValues, filterSeparatorValues;
|
|
|
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filterSeparatorValues.insert(1, Pose3().compose(poseError));
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filterSeparatorValues.insert(2, filterSeparatorValues.at<Pose3>(1).compose(poseOdometry).compose(poseError));
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filterSumarization.push_back(LinearContainerFactor(PriorFactor<Pose3>(1, poseInitial, noisePrior).linearize(filterSeparatorValues), filterSeparatorValues));
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filterSumarization.push_back(LinearContainerFactor(BetweenFactor<Pose3>(1, 2, poseOdometry, noiseOdometery).linearize(filterSeparatorValues), filterSeparatorValues));
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smootherFactors.push_back(BetweenFactor<Pose3>(2, 3, poseOdometry, noiseOdometery));
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smootherFactors.push_back(BetweenFactor<Pose3>(3, 4, poseOdometry, noiseOdometery));
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smootherFactors.addPrior(4, poseInitial, noisePrior);
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smootherValues.insert(3, filterSeparatorValues.at<Pose3>(2).compose(poseOdometry).compose(poseError));
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smootherValues.insert(4, smootherValues.at<Pose3>(3).compose(poseOdometry).compose(poseError));
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|
|
|
// Create expected values: the smoother output will be empty for this case
|
|
NonlinearFactorGraph expectedSmootherSummarization;
|
|
Values expectedSmootherSeparatorValues;
|
|
|
|
NonlinearFactorGraph actualSmootherSummarization;
|
|
Values actualSmootherSeparatorValues;
|
|
smoother.presync();
|
|
smoother.getSummarizedFactors(actualSmootherSummarization, actualSmootherSeparatorValues);
|
|
smoother.synchronize(smootherFactors, smootherValues, filterSumarization, filterSeparatorValues);
|
|
smoother.postsync();
|
|
|
|
// Check
|
|
CHECK(assert_equal(expectedSmootherSummarization, actualSmootherSummarization, 1e-6));
|
|
CHECK(assert_equal(expectedSmootherSeparatorValues, actualSmootherSeparatorValues, 1e-6));
|
|
|
|
|
|
// Update the smoother
|
|
smoother.update();
|
|
|
|
smoother.presync();
|
|
smoother.getSummarizedFactors(actualSmootherSummarization, actualSmootherSeparatorValues);
|
|
|
|
// Check the optimized value of smoother state
|
|
NonlinearFactorGraph allFactors = smootherFactors;
|
|
Values allValues = smoother.getLinearizationPoint();
|
|
|
|
GaussianFactorGraph::shared_ptr LinFactorGraph = allFactors.linearize(allValues);
|
|
// GaussianSequentialSolver GSS = GaussianSequentialSolver(*LinFactorGraph);
|
|
// GaussianBayesNet::shared_ptr GBNsptr = GSS.eliminate();
|
|
|
|
KeySet allkeys = LinFactorGraph->keys();
|
|
for(const auto key_value: filterSeparatorValues) {
|
|
allkeys.erase(key_value.key);
|
|
}
|
|
KeyVector variables(allkeys.begin(), allkeys.end());
|
|
std::pair<GaussianBayesNet::shared_ptr, GaussianFactorGraph::shared_ptr> result = LinFactorGraph->eliminatePartialSequential(variables, EliminateCholesky);
|
|
|
|
expectedSmootherSummarization.resize(0);
|
|
for(const GaussianFactor::shared_ptr& factor: *result.second) {
|
|
expectedSmootherSummarization.push_back(LinearContainerFactor(factor, allValues));
|
|
}
|
|
|
|
CHECK(assert_equal(expectedSmootherSummarization, actualSmootherSummarization, 1e-6));
|
|
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|