240 lines
7.6 KiB
C++
240 lines
7.6 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 testIncrementalFixedLagSmoother.cpp
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* @brief Unit tests for the Incremental Fixed-Lag Smoother
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* @author Stephen Williams (swilliams8@gatech.edu)
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* @date May 23, 2012
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*/
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#include <gtsam_unstable/nonlinear/IncrementalFixedLagSmoother.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/geometry/Point2.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianFactorGraph.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/Symbol.h>
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#include <gtsam/base/debug.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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Key MakeKey(size_t index) { return Symbol('x', index); }
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/* ************************************************************************* */
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bool check_smoother(const NonlinearFactorGraph& fullgraph, const Values& fullinit, const IncrementalFixedLagSmoother& smoother, const Key& key) {
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GaussianFactorGraph linearized = *fullgraph.linearize(fullinit);
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VectorValues delta = linearized.optimize();
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Values fullfinal = fullinit.retract(delta);
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Point2 expected = fullfinal.at<Point2>(key);
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Point2 actual = smoother.calculateEstimate<Point2>(key);
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return assert_equal(expected, actual);
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}
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/* ************************************************************************* */
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TEST( IncrementalFixedLagSmoother, Example )
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{
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// Test the IncrementalFixedLagSmoother in a pure linear environment. Thus, full optimization and
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// the IncrementalFixedLagSmoother should be identical (even with the linearized approximations at
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// the end of the smoothing lag)
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SETDEBUG("IncrementalFixedLagSmoother update", true);
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// Set up parameters
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SharedDiagonal odometerNoise = noiseModel::Diagonal::Sigmas(Vector2(0.1, 0.1));
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SharedDiagonal loopNoise = noiseModel::Diagonal::Sigmas(Vector2(0.1, 0.1));
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// Create a Fixed-Lag Smoother
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typedef IncrementalFixedLagSmoother::KeyTimestampMap Timestamps;
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IncrementalFixedLagSmoother smoother(7.0, ISAM2Params());
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// Create containers to keep the full graph
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Values fullinit;
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NonlinearFactorGraph fullgraph;
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// i keeps track of the time step
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size_t i = 0;
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// Add a prior at time 0 and update the HMF
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{
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Key key0 = MakeKey(0);
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NonlinearFactorGraph newFactors;
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Values newValues;
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Timestamps newTimestamps;
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newFactors.push_back(PriorFactor<Point2>(key0, Point2(0.0, 0.0), odometerNoise));
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newValues.insert(key0, Point2(0.01, 0.01));
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newTimestamps[key0] = 0.0;
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fullgraph.push_back(newFactors);
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fullinit.insert(newValues);
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// Update the smoother
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smoother.update(newFactors, newValues, newTimestamps);
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// Check
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CHECK(check_smoother(fullgraph, fullinit, smoother, key0));
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++i;
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}
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// Add odometry from time 0 to time 5
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while(i <= 5) {
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Key key1 = MakeKey(i-1);
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Key key2 = MakeKey(i);
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NonlinearFactorGraph newFactors;
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Values newValues;
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Timestamps newTimestamps;
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newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
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newValues.insert(key2, Point2(double(i)+0.1, -0.1));
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newTimestamps[key2] = double(i);
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fullgraph.push_back(newFactors);
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fullinit.insert(newValues);
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// Update the smoother
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smoother.update(newFactors, newValues, newTimestamps);
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// Check
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CHECK(check_smoother(fullgraph, fullinit, smoother, key2));
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++i;
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}
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// Add odometry from time 5 to 6 to the HMF and a loop closure at time 5 to the TSM
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{
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// Add the odometry factor to the HMF
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Key key1 = MakeKey(i-1);
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Key key2 = MakeKey(i);
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NonlinearFactorGraph newFactors;
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Values newValues;
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Timestamps newTimestamps;
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newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
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newFactors.push_back(BetweenFactor<Point2>(MakeKey(2), MakeKey(5), Point2(3.5, 0.0), loopNoise));
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newValues.insert(key2, Point2(double(i)+0.1, -0.1));
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newTimestamps[key2] = double(i);
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fullgraph.push_back(newFactors);
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fullinit.insert(newValues);
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// Update the smoother
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smoother.update(newFactors, newValues, newTimestamps);
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// Check
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CHECK(check_smoother(fullgraph, fullinit, smoother, key2));
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++i;
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}
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// Add odometry from time 6 to time 15
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while(i <= 15) {
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Key key1 = MakeKey(i-1);
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Key key2 = MakeKey(i);
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NonlinearFactorGraph newFactors;
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Values newValues;
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Timestamps newTimestamps;
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newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
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newValues.insert(key2, Point2(double(i)+0.1, -0.1));
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newTimestamps[key2] = double(i);
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fullgraph.push_back(newFactors);
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fullinit.insert(newValues);
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// Update the smoother
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smoother.update(newFactors, newValues, newTimestamps);
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// Check
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CHECK(check_smoother(fullgraph, fullinit, smoother, key2));
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++i;
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}
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// add/remove an extra factor
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{
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Key key1 = MakeKey(i-1);
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Key key2 = MakeKey(i);
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NonlinearFactorGraph newFactors;
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Values newValues;
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Timestamps newTimestamps;
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// add 2 odometry factors
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newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
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newFactors.push_back(BetweenFactor<Point2>(key1, key2, Point2(1.0, 0.0), odometerNoise));
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newValues.insert(key2, Point2(double(i)+0.1, -0.1));
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newTimestamps[key2] = double(i);
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fullgraph.push_back(newFactors);
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fullinit.insert(newValues);
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// Update the smoother
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smoother.update(newFactors, newValues, newTimestamps);
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// Check
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CHECK(check_smoother(fullgraph, fullinit, smoother, key2));
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// now remove one of the two and try again
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// empty values and new factors for fake update in which we only remove factors
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NonlinearFactorGraph emptyNewFactors;
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Values emptyNewValues;
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Timestamps emptyNewTimestamps;
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size_t factorIndex = 25; // any index that does not break connectivity of the graph
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FactorIndices factorToRemove;
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factorToRemove.push_back(factorIndex);
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const NonlinearFactorGraph smootherFactorsBeforeRemove = smoother.getFactors();
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// remove factor
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smoother.update(emptyNewFactors, emptyNewValues, emptyNewTimestamps,factorToRemove);
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// Note: the following test (checking that the number of factor is reduced by 1)
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// fails since we are not reusing slots, hence also when removing a factor we do not change
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// the size of the factor graph
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// size_t nrFactorsAfterRemoval = smoother.getFactors().size();
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// DOUBLES_EQUAL(nrFactorsBeforeRemoval-1, nrFactorsAfterRemoval, 1e-5);
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// check that the factors in the smoother are right
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NonlinearFactorGraph actual = smoother.getFactors();
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for(size_t i=0; i< smootherFactorsBeforeRemove.size(); i++){
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// check that the factors that were not removed are there
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if(smootherFactorsBeforeRemove[i] && i != factorIndex){
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EXPECT(smootherFactorsBeforeRemove[i]->equals(*actual[i]));
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}
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else{ // while the factors that were not there or were removed are no longer there
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EXPECT(!actual[i]);
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}
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}
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}
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}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
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/* ************************************************************************* */
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