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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file testConcurrentBatchSmoother.cpp
* @brief Unit tests for the Concurrent Batch Smoother
* @author Stephen Williams (swilliams8@gatech.edu)
* @date Jan 5, 2013
*/
#include <gtsam_unstable/nonlinear/ConcurrentBatchSmoother.h>
#include <gtsam_unstable/nonlinear/LinearizedFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Ordering.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/nonlinear/Symbol.h>
#include <gtsam/nonlinear/Key.h>
#include <gtsam/inference/JunctionTree.h>
#include <gtsam/geometry/Pose3.h>
#include <CppUnitLite/TestHarness.h>
using namespace std;
using namespace gtsam;
namespace {
// Set up initial pose, odometry difference, loop closure difference, and initialization errors
const Pose3 poseInitial;
const Pose3 poseOdometry( Rot3::RzRyRx(Vector_(3, 0.05, 0.10, -0.75)), Point3(1.0, -0.25, 0.10) );
const Pose3 poseError( Rot3::RzRyRx(Vector_(3, 0.01, 0.02, -0.1)), Point3(0.05, -0.05, 0.02) );
// Set up noise models for the factors
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
const SharedDiagonal noiseOdometery = noiseModel::Diagonal::Sigmas(Vector_(6, 0.1, 0.1, 0.1, 0.5, 0.5, 0.5));
const SharedDiagonal noiseLoop = noiseModel::Diagonal::Sigmas(Vector_(6, 0.25, 0.25, 0.25, 1.0, 1.0, 1.0));
// Create a derived class to allow testing protected member functions
class ConcurrentBatchSmootherTester : public ConcurrentBatchSmoother {
public:
ConcurrentBatchSmootherTester(const LevenbergMarquardtParams& parameters) : ConcurrentBatchSmoother(parameters) { };
virtual ~ConcurrentBatchSmootherTester() { };
// Add accessors to the protected members
void presync() {
ConcurrentBatchSmoother::presync();
};
void getSummarizedFactors(NonlinearFactorGraph& summarizedFactors) {
ConcurrentBatchSmoother::getSummarizedFactors(summarizedFactors);
};
void synchronize(const NonlinearFactorGraph& smootherFactors, const Values& smootherValues, const NonlinearFactorGraph& summarizedFactors, const Values& rootValues) {
ConcurrentBatchSmoother::synchronize(smootherFactors, smootherValues, summarizedFactors, rootValues);
};
void postsync() {
ConcurrentBatchSmoother::postsync();
};
};
/* ************************************************************************* */
bool hessian_equal(const NonlinearFactorGraph& expected, const NonlinearFactorGraph& actual, const Values& theta, double tol = 1e-9) {
FastSet<Key> expectedKeys = expected.keys();
FastSet<Key> actualKeys = actual.keys();
// Verify the set of keys in both graphs are the same
if(!std::equal(expectedKeys.begin(), expectedKeys.end(), actualKeys.begin()))
return false;
// Create an ordering
Ordering ordering;
BOOST_FOREACH(Key key, expectedKeys) {
ordering.push_back(key);
}
// Linearize each factor graph
GaussianFactorGraph expectedGaussian;
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, expected) {
if(factor)
expectedGaussian.push_back( factor->linearize(theta, ordering) );
}
GaussianFactorGraph actualGaussian;
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, actual) {
if(factor)
actualGaussian.push_back( factor->linearize(theta, ordering) );
}
// Convert linear factor graph into a dense Hessian
Matrix expectedHessian = expectedGaussian.augmentedHessian();
Matrix actualHessian = actualGaussian.augmentedHessian();
// Zero out the lower-right entry. This corresponds to a constant in the optimization,
// which does not affect the result. Further, in conversions between Jacobians and Hessians,
// this term is ignored.
expectedHessian(expectedHessian.rows()-1, expectedHessian.cols()-1) = 0.0;
actualHessian(actualHessian.rows()-1, actualHessian.cols()-1) = 0.0;
// Compare Hessians
return assert_equal(expectedHessian, actualHessian, tol);
}
///* ************************************************************************* */
void CreateFactors(NonlinearFactorGraph& graph, Values& theta, size_t index1 = 0, size_t index2 = 1) {
// Calculate all poses
Pose3 poses[20];
poses[0] = poseInitial;
for(size_t index = 1; index < 20; ++index) {
poses[index] = poses[index-1].compose(poseOdometry);
}
// Create all keys
Key keys[20];
for(size_t index = 0; index < 20; ++index) {
keys[index] = Symbol('X', index);
}
// Create factors that will form a specific tree structure
// Loop over the included timestamps
for(size_t index = index1; index < index2; ++index) {
switch(index) {
case 0:
{
graph.add(PriorFactor<Pose3>(keys[0], poses[0], noisePrior));
// Add new variables
theta.insert(keys[0], poses[0].compose(poseError));
break;
}
case 1:
{
// Add odometry factor between 0 and 1
Pose3 poseDelta = poses[0].between(poses[1]);
graph.add(BetweenFactor<Pose3>(keys[0], keys[1], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[1], poses[1].compose(poseError));
break;
}
case 2:
{
break;
}
case 3:
{
// Add odometry factor between 1 and 3
Pose3 poseDelta = poses[1].between(poses[3]);
graph.add(BetweenFactor<Pose3>(keys[1], keys[3], poseDelta, noiseOdometery));
// Add odometry factor between 2 and 3
poseDelta = poses[2].between(poses[3]);
graph.add(BetweenFactor<Pose3>(keys[2], keys[3], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[2], poses[2].compose(poseError));
theta.insert(keys[3], poses[3].compose(poseError));
break;
}
case 4:
{
break;
}
case 5:
{
// Add odometry factor between 3 and 5
Pose3 poseDelta = poses[3].between(poses[5]);
graph.add(BetweenFactor<Pose3>(keys[3], keys[5], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[5], poses[5].compose(poseError));
break;
}
case 6:
{
// Add odometry factor between 3 and 6
Pose3 poseDelta = poses[3].between(poses[6]);
graph.add(BetweenFactor<Pose3>(keys[3], keys[6], poseDelta, noiseOdometery));
// Add odometry factor between 5 and 6
poseDelta = poses[5].between(poses[6]);
graph.add(BetweenFactor<Pose3>(keys[5], keys[6], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[6], poses[6].compose(poseError));
break;
}
case 7:
{
// Add odometry factor between 4 and 7
Pose3 poseDelta = poses[4].between(poses[7]);
graph.add(BetweenFactor<Pose3>(keys[4], keys[7], poseDelta, noiseOdometery));
// Add odometry factor between 6 and 7
poseDelta = poses[6].between(poses[7]);
graph.add(BetweenFactor<Pose3>(keys[6], keys[7], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[4], poses[4].compose(poseError));
theta.insert(keys[7], poses[7].compose(poseError));
break;
}
case 8:
break;
case 9:
{
// Add odometry factor between 6 and 9
Pose3 poseDelta = poses[6].between(poses[9]);
graph.add(BetweenFactor<Pose3>(keys[6], keys[9], poseDelta, noiseOdometery));
// Add odometry factor between 7 and 9
poseDelta = poses[7].between(poses[9]);
graph.add(BetweenFactor<Pose3>(keys[7], keys[9], poseDelta, noiseOdometery));
// Add odometry factor between 8 and 9
poseDelta = poses[8].between(poses[9]);
graph.add(BetweenFactor<Pose3>(keys[8], keys[9], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[8], poses[8].compose(poseError));
theta.insert(keys[9], poses[9].compose(poseError));
break;
}
case 10:
{
// Add odometry factor between 9 and 10
Pose3 poseDelta = poses[9].between(poses[10]);
graph.add(BetweenFactor<Pose3>(keys[9], keys[10], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[10], poses[10].compose(poseError));
break;
}
case 11:
{
// Add odometry factor between 10 and 11
Pose3 poseDelta = poses[10].between(poses[11]);
graph.add(BetweenFactor<Pose3>(keys[10], keys[11], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[11], poses[11].compose(poseError));
break;
}
case 12:
{
// Add odometry factor between 7 and 12
Pose3 poseDelta = poses[7].between(poses[12]);
graph.add(BetweenFactor<Pose3>(keys[7], keys[12], poseDelta, noiseOdometery));
// Add odometry factor between 9 and 12
poseDelta = poses[9].between(poses[12]);
graph.add(BetweenFactor<Pose3>(keys[9], keys[12], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[12], poses[12].compose(poseError));
break;
}
case 13:
{
// Add odometry factor between 10 and 13
Pose3 poseDelta = poses[10].between(poses[13]);
graph.add(BetweenFactor<Pose3>(keys[10], keys[13], poseDelta, noiseOdometery));
// Add odometry factor between 12 and 13
poseDelta = poses[12].between(poses[13]);
graph.add(BetweenFactor<Pose3>(keys[12], keys[13], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[13], poses[13].compose(poseError));
break;
}
case 14:
{
// Add odometry factor between 11 and 14
Pose3 poseDelta = poses[11].between(poses[14]);
graph.add(BetweenFactor<Pose3>(keys[11], keys[14], poseDelta, noiseOdometery));
// Add odometry factor between 13 and 14
poseDelta = poses[13].between(poses[14]);
graph.add(BetweenFactor<Pose3>(keys[13], keys[14], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[14], poses[14].compose(poseError));
break;
}
case 15:
break;
case 16:
{
// Add odometry factor between 13 and 16
Pose3 poseDelta = poses[13].between(poses[16]);
graph.add(BetweenFactor<Pose3>(keys[13], keys[16], poseDelta, noiseOdometery));
// Add odometry factor between 14 and 16
poseDelta = poses[14].between(poses[16]);
graph.add(BetweenFactor<Pose3>(keys[14], keys[16], poseDelta, noiseOdometery));
// Add odometry factor between 15 and 16
poseDelta = poses[15].between(poses[16]);
graph.add(BetweenFactor<Pose3>(keys[15], keys[16], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[15], poses[15].compose(poseError));
theta.insert(keys[16], poses[16].compose(poseError));
break;
}
case 17:
{
// Add odometry factor between 16 and 17
Pose3 poseDelta = poses[16].between(poses[17]);
graph.add(BetweenFactor<Pose3>(keys[16], keys[17], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[17], poses[17].compose(poseError));
break;
}
case 18:
{
// Add odometry factor between 17 and 18
Pose3 poseDelta = poses[17].between(poses[18]);
graph.add(BetweenFactor<Pose3>(keys[17], keys[18], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[18], poses[18].compose(poseError));
break;
}
case 19:
{
// Add odometry factor between 14 and 19
Pose3 poseDelta = poses[14].between(poses[19]);
graph.add(BetweenFactor<Pose3>(keys[14], keys[19], poseDelta, noiseOdometery));
// Add odometry factor between 16 and 19
poseDelta = poses[16].between(poses[19]);
graph.add(BetweenFactor<Pose3>(keys[16], keys[19], poseDelta, noiseOdometery));
// Add new variables
theta.insert(keys[19], poses[19].compose(poseError));
break;
}
}
}
return;
}
/* ************************************************************************* */
Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, const Values& rootValues = Values()) {
// Create an L-M optimizer
LevenbergMarquardtParams parameters;
parameters.linearSolverType = SuccessiveLinearizationParams::MULTIFRONTAL_QR;
LevenbergMarquardtOptimizer optimizer(graph, theta, parameters);
// Use a custom optimization loop so the linearization points can be controlled
double currentError;
do {
// Force variables associated with root keys to keep the same linearization point
if(rootValues.size() > 0) {
// Put the old values of the root keys back into the optimizer state
optimizer.state().values.update(rootValues);
// Update the error value with the new theta
optimizer.state().error = graph.error(optimizer.state().values);
}
// Do next iteration
currentError = optimizer.error();
optimizer.iterate();
} while(optimizer.iterations() < parameters.maxIterations &&
!checkConvergence(parameters.relativeErrorTol, parameters.absoluteErrorTol,
parameters.errorTol, currentError, optimizer.error(), parameters.verbosity));
// return the final optimized values
return optimizer.values();
}
/* ************************************************************************* */
void FindFactorsWithAny(const std::set<Key>& keys, const NonlinearFactorGraph& sourceFactors, NonlinearFactorGraph& destinationFactors) {
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, sourceFactors) {
NonlinearFactor::const_iterator key = factor->begin();
while((key != factor->end()) && (!std::binary_search(keys.begin(), keys.end(), *key))) {
++key;
}
if(key != factor->end()) {
destinationFactors.push_back(factor);
}
}
}
/* ************************************************************************* */
void FindFactorsWithOnly(const std::set<Key>& keys, const NonlinearFactorGraph& sourceFactors, NonlinearFactorGraph& destinationFactors) {
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, sourceFactors) {
NonlinearFactor::const_iterator key = factor->begin();
while((key != factor->end()) && (std::binary_search(keys.begin(), keys.end(), *key))) {
++key;
}
if(key == factor->end()) {
destinationFactors.push_back(factor);
}
}
}
/* ************************************************************************* */
typedef BayesTree<GaussianConditional,ISAM2Clique>::sharedClique Clique;
void SymbolicPrintTree(const Clique& clique, const Ordering& ordering, const std::string indent = "") {
std::cout << indent << "P( ";
BOOST_FOREACH(Index index, clique->conditional()->frontals()){
std::cout << DefaultKeyFormatter(ordering.key(index)) << " ";
}
if(clique->conditional()->nrParents() > 0) {
std::cout << "| ";
}
BOOST_FOREACH(Index index, clique->conditional()->parents()){
std::cout << DefaultKeyFormatter(ordering.key(index)) << " ";
}
std::cout << ")" << std::endl;
BOOST_FOREACH(const Clique& child, clique->children()) {
SymbolicPrintTree(child, ordering, indent+" ");
}
}
}
/* ************************************************************************* */
TEST_UNSAFE( ConcurrentBatchSmoother, update_Batch )
{
// Test the 'update' function of the ConcurrentBatchSmoother in a nonlinear environment.
// Thus, a full L-M optimization and the ConcurrentBatchSmoother results should be identical
// This tests adds all of the factors to the smoother at once (i.e. batch)
// Create a set of optimizer parameters
LevenbergMarquardtParams parameters;
// Create a Concurrent Batch Smoother
ConcurrentBatchSmoother smoother(parameters);
// Create containers to keep the full graph
Values fullTheta;
NonlinearFactorGraph fullGraph;
// Create all factors
CreateFactors(fullGraph, fullTheta, 0, 20);
// Optimize with Concurrent Batch Smoother
smoother.update(fullGraph, fullTheta);
Values actual = smoother.calculateEstimate();
// Optimize with L-M
Values expected = BatchOptimize(fullGraph, fullTheta);
// Check smoother versus batch
CHECK(assert_equal(expected, actual, 1e-4));
}
/* ************************************************************************* */
TEST_UNSAFE( ConcurrentBatchSmoother, update_Incremental )
{
// Test the 'update' function of the ConcurrentBatchSmoother in a nonlinear environment.
// Thus, a full L-M optimization and the ConcurrentBatchSmoother results should be identical
// This tests adds the factors to the smoother as they are created (i.e. incrementally)
// Create a set of optimizer parameters
LevenbergMarquardtParams parameters;
// Create a Concurrent Batch Smoother
ConcurrentBatchSmoother smoother(parameters);
// Create containers to keep the full graph
Values fullTheta;
NonlinearFactorGraph fullGraph;
// Add odometry from time 0 to time 10
for(size_t i = 0; i < 20; ++i) {
// Create containers to keep the new factors
Values newTheta;
NonlinearFactorGraph newGraph;
// Create factors
CreateFactors(newGraph, newTheta, i, i+1);
// Add these entries to the filter
smoother.update(newGraph, newTheta);
Values actual = smoother.calculateEstimate();
// Add these entries to the full batch version
fullGraph.push_back(newGraph);
fullTheta.insert(newTheta);
Values expected = BatchOptimize(fullGraph, fullTheta);
fullTheta = expected;
// Compare filter solution with full batch
CHECK(assert_equal(expected, actual, 1e-4));
}
}
/* ************************************************************************* */
TEST_UNSAFE( ConcurrentBatchSmoother, synchronize )
{
// Test the 'synchronize' function of the ConcurrentBatchSmoother in a nonlinear environment.
// The smoother is operating on a known tree structure, so the factors and summarization can
// be predicted for testing purposes
// Create a set of optimizer parameters
LevenbergMarquardtParams parameters;
// Create a Concurrent Batch Smoother
ConcurrentBatchSmootherTester smoother(parameters);
// Create containers to keep the full graph
Values fullTheta;
NonlinearFactorGraph fullGraph;
// Create factors for times 0 - 12
// When eliminated with ordering (X2 X0 X1 X4 X5 X3 X6 X8 X11 X10 X7 X9 X12)augmentedHessian
// ... this Bayes Tree is produced:
// Bayes Tree:
// P( X7 X9 X12 )
// P( X10 | X9 )
// P( X11 | X10 )
// P( X8 | X9 )
// P( X6 | X7 X9 )
// P( X5 X3 | X6 )
// P( X1 | X3 )
// P( X0 | X1 )
// P( X2 | X3 )
// P( X4 | X7 )
// We then produce the inputs necessary for the 'synchronize' function.
// The smoother is branches X4 and X6, the filter is branches X8 and X10, and the root is (X7 X9 X12)
CreateFactors(fullGraph, fullTheta, 0, 13);
// Optimize the full graph
Values optimalTheta = BatchOptimize(fullGraph, fullTheta);
// Re-eliminate to create the Bayes Tree
Ordering ordering;
ordering.push_back(Symbol('X', 2));
ordering.push_back(Symbol('X', 0));
ordering.push_back(Symbol('X', 1));
ordering.push_back(Symbol('X', 4));
ordering.push_back(Symbol('X', 5));
ordering.push_back(Symbol('X', 3));
ordering.push_back(Symbol('X', 6));
ordering.push_back(Symbol('X', 8));
ordering.push_back(Symbol('X', 11));
ordering.push_back(Symbol('X', 10));
ordering.push_back(Symbol('X', 7));
ordering.push_back(Symbol('X', 9));
ordering.push_back(Symbol('X', 12));
Values linpoint;
linpoint.insert(optimalTheta);
GaussianFactorGraph linearGraph = *fullGraph.linearize(linpoint, ordering);
JunctionTree<GaussianFactorGraph, ISAM2Clique> jt(linearGraph);
ISAM2Clique::shared_ptr root = jt.eliminate(EliminateQR);
BayesTree<GaussianConditional, ISAM2Clique> bayesTree;
bayesTree.insert(root);
// Extract the values for the smoother keys. This consists of the branches: X4 and X6
// Extract the non-root values from the initial values to test the smoother optimization
Values smootherValues;
smootherValues.insert(Symbol('X', 0), fullTheta.at(Symbol('X', 0)));
smootherValues.insert(Symbol('X', 1), fullTheta.at(Symbol('X', 1)));
smootherValues.insert(Symbol('X', 2), fullTheta.at(Symbol('X', 2)));
smootherValues.insert(Symbol('X', 3), fullTheta.at(Symbol('X', 3)));
smootherValues.insert(Symbol('X', 4), fullTheta.at(Symbol('X', 4)));
smootherValues.insert(Symbol('X', 5), fullTheta.at(Symbol('X', 5)));
smootherValues.insert(Symbol('X', 6), fullTheta.at(Symbol('X', 6)));
// Extract the optimal root values
Values rootValues;
rootValues.insert(Symbol('X', 7), optimalTheta.at(Symbol('X', 7)));
rootValues.insert(Symbol('X', 9), optimalTheta.at(Symbol('X', 9)));
rootValues.insert(Symbol('X', 12), optimalTheta.at(Symbol('X', 12)));
// Extract the nonlinear smoother factors as any factor with a non-root smoother key
std::set<Key> smootherKeys;
smootherKeys.insert(Symbol('X', 0));
smootherKeys.insert(Symbol('X', 1));
smootherKeys.insert(Symbol('X', 2));
smootherKeys.insert(Symbol('X', 3));
smootherKeys.insert(Symbol('X', 4));
smootherKeys.insert(Symbol('X', 5));
smootherKeys.insert(Symbol('X', 6));
NonlinearFactorGraph smootherFactors;
FindFactorsWithAny(smootherKeys, fullGraph, smootherFactors);
// Extract the filter summarized factors. This consists of the linear cached factors from
// the filter branches X8 and X10, as well as any nonlinear factor that involves only root keys
NonlinearFactorGraph filterSummarization;
filterSummarization.add(LinearizedJacobianFactor(boost::static_pointer_cast<JacobianFactor>(bayesTree.nodes().at(ordering.at(Symbol('X', 8)))->cachedFactor()), ordering, linpoint));
filterSummarization.add(LinearizedJacobianFactor(boost::static_pointer_cast<JacobianFactor>(bayesTree.nodes().at(ordering.at(Symbol('X', 10)))->cachedFactor()), ordering, linpoint));
std::set<Key> rootKeys;
rootKeys.insert(Symbol('X', 7));
rootKeys.insert(Symbol('X', 9));
rootKeys.insert(Symbol('X', 12));
FindFactorsWithOnly(rootKeys, fullGraph, filterSummarization);
// Perform the synchronization procedure
NonlinearFactorGraph actualSmootherSummarization;
smoother.presync();
smoother.getSummarizedFactors(actualSmootherSummarization);
smoother.synchronize(smootherFactors, smootherValues, filterSummarization, rootValues);
smoother.postsync();
// Verify the returned smoother values is empty in the first iteration
NonlinearFactorGraph expectedSmootherSummarization;
CHECK(assert_equal(expectedSmootherSummarization, actualSmootherSummarization, 1e-4));
// Perform a full update of the smoother. Since the root values/summarized filter factors were
// created at the optimal values, the smoother should be identical to the batch optimization
smoother.update();
Values actualSmootherTheta = smoother.calculateEstimate();
// Create the expected values as the optimal set
Values expectedSmootherTheta;
expectedSmootherTheta.insert(Symbol('X', 0), optimalTheta.at(Symbol('X', 0)));
expectedSmootherTheta.insert(Symbol('X', 1), optimalTheta.at(Symbol('X', 1)));
expectedSmootherTheta.insert(Symbol('X', 2), optimalTheta.at(Symbol('X', 2)));
expectedSmootherTheta.insert(Symbol('X', 3), optimalTheta.at(Symbol('X', 3)));
expectedSmootherTheta.insert(Symbol('X', 4), optimalTheta.at(Symbol('X', 4)));
expectedSmootherTheta.insert(Symbol('X', 5), optimalTheta.at(Symbol('X', 5)));
expectedSmootherTheta.insert(Symbol('X', 6), optimalTheta.at(Symbol('X', 6)));
// Compare filter solution with full batch
CHECK(assert_equal(expectedSmootherTheta, actualSmootherTheta, 1e-4));
// Add a loop closure factor to the smoother and re-check. Since the filter
// factors were created at the optimal linpoint, and since the new loop closure
// does not involve filter keys, the smoother should still yeild the optimal solution
// The new Bayes Tree is:
// Bayes Tree:
// P( X7 X9 X12 )
// P( X10 | X9 )
// P( X11 | X10 )
// P( X8 | X9 )
// P( X6 | X7 X9 )
// P( X4 | X6 X7 )
// P( X3 X5 | X4 X6 )
// P( X2 | X3 )
// P( X1 | X3 X4 )
// P( X0 | X1 )
Pose3 poseDelta = fullTheta.at<Pose3>(Symbol('X', 1)).between(fullTheta.at<Pose3>(Symbol('X', 4)));
NonlinearFactor::shared_ptr loopClosure = NonlinearFactor::shared_ptr(new BetweenFactor<Pose3>(Symbol('X', 1), Symbol('X', 4), poseDelta, noiseOdometery));
fullGraph.push_back(loopClosure);
optimalTheta = BatchOptimize(fullGraph, fullTheta, rootValues);
// Recreate the Bayes Tree
linpoint.clear();
linpoint.insert(optimalTheta);
linpoint.update(rootValues);
linearGraph = *fullGraph.linearize(linpoint, ordering);
jt = JunctionTree<GaussianFactorGraph, ISAM2Clique>(linearGraph);
root = jt.eliminate(EliminateQR);
bayesTree = BayesTree<GaussianConditional, ISAM2Clique>();
bayesTree.insert(root);
// Add the loop closure to the smoother
NonlinearFactorGraph newFactors;
newFactors.push_back(loopClosure);
smoother.update(newFactors);
actualSmootherTheta = smoother.calculateEstimate();
// Create the expected values as the optimal set
expectedSmootherTheta.clear();
expectedSmootherTheta.insert(Symbol('X', 0), optimalTheta.at(Symbol('X', 0)));
expectedSmootherTheta.insert(Symbol('X', 1), optimalTheta.at(Symbol('X', 1)));
expectedSmootherTheta.insert(Symbol('X', 2), optimalTheta.at(Symbol('X', 2)));
expectedSmootherTheta.insert(Symbol('X', 3), optimalTheta.at(Symbol('X', 3)));
expectedSmootherTheta.insert(Symbol('X', 4), optimalTheta.at(Symbol('X', 4)));
expectedSmootherTheta.insert(Symbol('X', 5), optimalTheta.at(Symbol('X', 5)));
expectedSmootherTheta.insert(Symbol('X', 6), optimalTheta.at(Symbol('X', 6)));
// Compare filter solution with full batch
// TODO: Check This
// CHECK(assert_equal(expectedSmootherTheta, actualSmootherTheta, 1e-4));
// Now perform a second synchronization to test the smoother-calculated summarization
actualSmootherSummarization.resize(0);
smootherFactors.resize(0);
smootherValues.clear();
smoother.presync();
smoother.getSummarizedFactors(actualSmootherSummarization);
smoother.synchronize(smootherFactors, smootherValues, filterSummarization, rootValues);
smoother.postsync();
// Extract the expected smoother summarization from the Bayes Tree
// The smoother branches after the addition of the loop closure is only X6
expectedSmootherSummarization.resize(0);
JacobianFactor::shared_ptr jf = boost::dynamic_pointer_cast<JacobianFactor>(bayesTree.nodes().at(ordering.at(Symbol('X', 6)))->cachedFactor());
LinearizedJacobianFactor::shared_ptr ljf(new LinearizedJacobianFactor(jf, ordering, linpoint));
expectedSmootherSummarization.push_back(ljf);
// Compare smoother factors with the expected factors by computing the hessian information matrix
// TODO: Check This
// CHECK(hessian_equal(expectedSmootherSummarization, actualSmootherSummarization, linpoint, 1e-4));
// TODO: Modify the second synchronization so that the filter sends an additional set of factors.
// I'm not sure what additional code this will exercise, but just for good measure.
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */
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