testIncrementalFixedLagSmoother.cpp

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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
 
 * -------------------------------------------------------------------------- */
 
#include <gtsam_unstable/nonlinear/IncrementalFixedLagSmoother.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/inference/Key.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/base/debug.h>
 
#include <CppUnitLite/TestHarness.h>
 
using namespace std;
using namespace gtsam;
 
Key MakeKey(size_t index) { return Symbol('x', index); }
 
/* ************************************************************************* */
bool check_smoother(const NonlinearFactorGraph& fullgraph, const Values& fullinit, const IncrementalFixedLagSmoother& smoother, const Key& key) {
 
  GaussianFactorGraph linearized = *fullgraph.linearize(fullinit);
  VectorValues delta = linearized.optimize();
  Values fullfinal = fullinit.retract(delta);
 
  Point2 expected = fullfinal.at<Point2>(key);
  Point2 actual = smoother.calculateEstimate<Point2>(key);
 
  return assert_equal(expected, actual);
}
 
/* ************************************************************************* */
TEST( IncrementalFixedLagSmoother, Example )
{
  // Test the IncrementalFixedLagSmoother in a pure linear environment. Thus, full optimization and
  // the IncrementalFixedLagSmoother should be identical (even with the linearized approximations at
  // the end of the smoothing lag)
 
  SETDEBUG("IncrementalFixedLagSmoother update", true);
 
  // Set up parameters
  SharedDiagonal odometerNoise = noiseModel::Diagonal::Sigmas(Vector2(0.1, 0.1));
  SharedDiagonal loopNoise = noiseModel::Diagonal::Sigmas(Vector2(0.1, 0.1));
 
  // Create a Fixed-Lag Smoother
  typedef IncrementalFixedLagSmoother::KeyTimestampMap Timestamps;
  IncrementalFixedLagSmoother smoother(7.0, ISAM2Params());
 
  // Create containers to keep the full graph
  Values fullinit;
  NonlinearFactorGraph fullgraph;
 
  // i keeps track of the time step
  size_t i = 0;
 
  // Add a prior at time 0 and update the HMF
  {
    Key key0 = MakeKey(0);
 
    NonlinearFactorGraph newFactors;
    Values newValues;
    Timestamps newTimestamps;
 
    newFactors.addPrior(key0, Point2(0.0, 0.0), odometerNoise);
    newValues.insert(key0, Point2(0.01, 0.01));
    newTimestamps[key0] = 0.0;
 
    fullgraph.push_back(newFactors);
    fullinit.insert(newValues);
 
    // Update the smoother
    smoother.update(newFactors, newValues, newTimestamps);
 
    // Check
    CHECK(check_smoother(fullgraph, fullinit, smoother, key0));
 
    ++i;
  }
 
  // Add odometry from time 0 to time 5
  while(i <= 5) {
    Key key1 = MakeKey(i-1);
    Key key2 = MakeKey(i);
 
    NonlinearFactorGraph newFactors;
    Values newValues;
    Timestamps newTimestamps;
 
    newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
    newValues.insert(key2, Point2(double(i)+0.1, -0.1));
    newTimestamps[key2] = double(i);
 
    fullgraph.push_back(newFactors);
    fullinit.insert(newValues);
 
    // Update the smoother
    smoother.update(newFactors, newValues, newTimestamps);
 
    // Check
    CHECK(check_smoother(fullgraph, fullinit, smoother, key2));
 
    ++i;
  }
 
  // Add odometry from time 5 to 6 to the HMF and a loop closure at time 5 to the TSM
  {
    // Add the odometry factor to the HMF
    Key key1 = MakeKey(i-1);
    Key key2 = MakeKey(i);
 
    NonlinearFactorGraph newFactors;
    Values newValues;
    Timestamps newTimestamps;
 
    newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
    newFactors.push_back(BetweenFactor<Point2>(MakeKey(2), MakeKey(5), Point2(3.5, 0.0), loopNoise));
    newValues.insert(key2, Point2(double(i)+0.1, -0.1));
    newTimestamps[key2] = double(i);
 
    fullgraph.push_back(newFactors);
    fullinit.insert(newValues);
 
    // Update the smoother
    smoother.update(newFactors, newValues, newTimestamps);
 
    // Check
    CHECK(check_smoother(fullgraph, fullinit, smoother, key2));
 
    ++i;
  }
 
  // Add odometry from time 6 to time 15
  while(i <= 15) {
    Key key1 = MakeKey(i-1);
    Key key2 = MakeKey(i);
 
    NonlinearFactorGraph newFactors;
    Values newValues;
    Timestamps newTimestamps;
 
    newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
    newValues.insert(key2, Point2(double(i)+0.1, -0.1));
    newTimestamps[key2] = double(i);
 
    fullgraph.push_back(newFactors);
    fullinit.insert(newValues);
 
    // Update the smoother
    smoother.update(newFactors, newValues, newTimestamps);
 
    // Check
    CHECK(check_smoother(fullgraph, fullinit, smoother, key2));
 
    ++i;
  }
 
  // add/remove an extra factor
  {
          Key key1 = MakeKey(i-1);
          Key key2 = MakeKey(i);
 
          NonlinearFactorGraph newFactors;
          Values newValues;
          Timestamps newTimestamps;
 
          // add 2 odometry factors
          newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
          newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
          newValues.insert(key2, Point2(double(i)+0.1, -0.1));
          newTimestamps[key2] = double(i);
 
          fullgraph.push_back(newFactors);
          fullinit.insert(newValues);
 
          // Update the smoother
          smoother.update(newFactors, newValues, newTimestamps);
 
          // Check
          CHECK(check_smoother(fullgraph, fullinit, smoother, key2));
 
          // now remove one of the two and try again
          // empty values and new factors for fake update in which we only remove factors
          NonlinearFactorGraph emptyNewFactors;
          Values emptyNewValues;
          Timestamps emptyNewTimestamps;
 
          size_t factorIndex = 25; // any index that does not break connectivity of the graph
          FactorIndices factorToRemove;
          factorToRemove.push_back(factorIndex);
 
          const NonlinearFactorGraph smootherFactorsBeforeRemove = smoother.getFactors();
 
          // remove factor
          smoother.update(emptyNewFactors, emptyNewValues, emptyNewTimestamps,factorToRemove);
 
          // Note: the following test (checking that the number of factor is reduced by 1)
          // fails  since we are not reusing slots, hence also when removing a factor we do not change
          // the size of the factor graph
          // size_t nrFactorsAfterRemoval = smoother.getFactors().size();
          // DOUBLES_EQUAL(nrFactorsBeforeRemoval-1, nrFactorsAfterRemoval, 1e-5);
 
          // check that the factors in the smoother are right
          NonlinearFactorGraph actual = smoother.getFactors();
          for(size_t i=0; i< smootherFactorsBeforeRemove.size(); i++){
            // check that the factors that were not removed are there
            if(smootherFactorsBeforeRemove[i] && i != factorIndex){
              EXPECT(smootherFactorsBeforeRemove[i]->equals(*actual[i]));
            }
            else{ // while the factors that were not there or were removed are no longer there
              EXPECT(!actual[i]);
            }
          }
  }
}
 
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */