152 lines
4.6 KiB
C++
152 lines
4.6 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 testTOAFactor.cpp
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* @brief Unit tests for "Time of Arrival" factor
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* @author Frank Dellaert
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* @author Jay Chakravarty
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* @date December 2014
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*/
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#include <gtsam_unstable/nonlinear/ExpressionFactor.h>
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#include <gtsam_unstable/geometry/Event.h>
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namespace gtsam {
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/// A "Time of Arrival" factor - so little code seems hardly worth it :-)
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class TOAFactor: public ExpressionFactor<double> {
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typedef Expression<double> double_;
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public:
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/**
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* Constructor
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* @param some expression yielding an event
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* @param microphone_ expression yielding a microphone location
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* @param toaMeasurement time of arrival at microphone
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* @param model noise model
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*/
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TOAFactor(const Expression<Event>& event_,
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const Expression<Point3>& microphone_, double toaMeasurement,
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const SharedNoiseModel& model) :
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ExpressionFactor<double>(model, toaMeasurement,
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double_(&Event::toa, event_, microphone_)) {
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}
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};
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} //\ namespace gtsam
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <CppUnitLite/TestHarness.h>
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#include <boost/bind.hpp>
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using namespace std;
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using namespace gtsam;
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// Create a noise model for the TOA error
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static const double ms = 1e-3;
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static const double cm = 1e-2;
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typedef Eigen::Matrix<double, 1, 1> Vector1;
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static SharedNoiseModel model(noiseModel::Isotropic::Sigma(1,0.5*ms));
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static const double timeOfEvent = 25;
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static const Event exampleEvent(timeOfEvent, 1, 0, 0);
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static const Point3 microphoneAt0;
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//*****************************************************************************
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TEST( TOAFactor, Construct ) {
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Key key = 12;
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Expression<Event> event_(key);
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Expression<Point3> knownMicrophone_(microphoneAt0); // constant expression
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double measurement = 7;
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TOAFactor factor(event_, knownMicrophone_, measurement, model);
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}
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//*****************************************************************************
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TEST( TOAFactor, WholeEnchilada ) {
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static const bool verbose = false;
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// Create microphones
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const double height = 0.5;
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vector<Point3> microphones;
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microphones.push_back(Point3(0, 0, height));
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microphones.push_back(Point3(403 * cm, 0, height));
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microphones.push_back(Point3(403 * cm, 403 * cm, height));
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microphones.push_back(Point3(0, 403 * cm, height));
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EXPECT_LONGS_EQUAL(4, microphones.size());
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microphones.push_back(Point3(200*cm, 200 * cm, height));
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// Create a ground truth point
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const double timeOfEvent = 0;
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Event groundTruthEvent(timeOfEvent, 245 * cm, 201.5 * cm, (212 - 45) * cm);
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// Simulate measurements
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size_t K = microphones.size();
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vector<double> measurements(K);
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for (size_t i = 0; i < K; i++) {
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measurements[i] = groundTruthEvent.toa(microphones[i]);
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if (verbose) {
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cout << "mic" << i << " = " << microphones[i] << endl;
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cout << "z" << i << " = " << measurements[i]/ms << endl;
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}
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}
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// Now, estimate using non-linear optimization
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NonlinearFactorGraph graph;
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Key key = 12;
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Expression<Event> event_(key);
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for (size_t i = 0; i < K; i++) {
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Expression<Point3> knownMicrophone_(microphones[i]); // constant expression
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graph.add(TOAFactor(event_, knownMicrophone_, measurements[i], model));
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}
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/// Print the graph
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if (verbose)
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GTSAM_PRINT(graph);
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// Create initial estimate
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Values initialEstimate;
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//Event estimatedEvent(timeOfEvent -10, 200 * cm, 150 * cm, 350 * cm);
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Vector4 delta; delta << 0.1, 0.1, -0.1, 0.1;
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Event estimatedEvent = groundTruthEvent.retract(delta);
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initialEstimate.insert(key, estimatedEvent);
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// Print
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if (verbose)
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initialEstimate.print("Initial Estimate:\n");
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// Optimize using Levenberg-Marquardt optimization.
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LevenbergMarquardtParams params;
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params.setAbsoluteErrorTol(1e-10);
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if (verbose)
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params.setVerbosity("ERROR");
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LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, params);
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Values result = optimizer.optimize();
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if (verbose)
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result.print("Final Result:\n");
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EXPECT(assert_equal(groundTruthEvent, result.at<Event>(key), 1e-6));
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}
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//*****************************************************************************
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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//*****************************************************************************
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