/* ---------------------------------------------------------------------------- * 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 testTOAFactor.cpp * @brief Unit tests for "Time of Arrival" factor * @author Frank Dellaert * @author Jay Chakravarty * @date December 2014 */ #include #include #include #include namespace gtsam { /// A space-time event class Event { double time_; ///< Time event was generated Point3 location_; ///< Location at time event was generated public: /// Speed of sound static const double Speed; /// Default Constructor Event() : time_(0) { } /// Constructor from time and location Event(double t, const Point3& p) : time_(t), location_(p) { } /// Constructor with doubles Event(double t, double x, double y, double z) : time_(t), location_(x, y, z) { } /** print with optional string */ void print(const std::string& s = "") const { std::cout << s << ", time = " << time_ << std::endl; location_.print("location"); } /** equals with an tolerance */ bool equals(const Event& other, double tol = 1e-9) const { return std::abs(time_ - other.time_) < tol && location_.equals(other.location_, tol); } /// Manifold stuff: size_t dim() const { return 4; } static size_t Dim() { return 4; } /// Updates a with tangent space delta inline Event retract(const Vector4& v) const { return Event(time_ + v[0], location_.retract(v.tail(3))); } /// Returns inverse retraction inline Vector4 localCoordinates(const Event& q) const { return Vector4::Zero(); // TODO } /// Time of arrival to given microphone double toa(const Point3& microphone, // OptionalJacobian<1, 4> H1 = boost::none, // OptionalJacobian<1, 3> H2 = boost::none) const { Matrix13 D1, D2; double distance = location_.distance(microphone, D1, D2); if (H1) { // derivative of toa with respect to event *H1 << 1, D1 / Speed; } if (H2) { // derivative of toa with respect to microphone location *H2 << D2 / Speed; } return time_ + distance / Speed; } }; const double Event::Speed = 330; // Define GTSAM traits namespace traits { template<> struct GTSAM_EXPORT dimension : public boost::integral_constant { }; } /// A "Time of Arrival" factor class TOAFactor: public ExpressionFactor { typedef Expression double_; public: /** * Constructor * @param some expression yielding an event * @param microphone_ expression yielding a microphone location * @param toaMeasurement time of arrival at microphone * @param model noise model */ TOAFactor(const Expression& event_, const Expression& microphone_, double toaMeasurement, const SharedNoiseModel& model) : ExpressionFactor(model, toaMeasurement, double_(&Event::toa, event_, microphone_)) { } }; } //\ namespace gtsam #include #include #include #include using namespace std; using namespace gtsam; // Create a noise model for the TOA error //static const double ms = 1e-3; static const double cm = 1e-2; typedef Eigen::Matrix Vector1; static SharedNoiseModel model(noiseModel::Unit::Create(1)); //***************************************************************************** TEST( Event, Constructor ) { const double t = 0; Event actual(t, 201.5 * cm, 201.5 * cm, (212 - 45) * cm); } //***************************************************************************** TEST( Event, Toa1 ) { Point3 microphone; Event event(0, 1, 0, 0); double expected = 1 / Event::Speed; EXPECT_DOUBLES_EQUAL(expected, event.toa(microphone), 1e-9); } //***************************************************************************** TEST( Event, Toa2 ) { Point3 microphone; double timeOfEvent = 25; Event event(timeOfEvent, 1, 0, 0); double expectedTOA = timeOfEvent + 1 / Event::Speed; EXPECT_DOUBLES_EQUAL(expectedTOA, event.toa(microphone), 1e-9); } //***************************************************************************** TEST( Event, Expression ) { Key key = 12; Expression event_(key); Point3 microphone; Expression knownMicrophone_(microphone); // constant expression Expression expression(&Event::toa, event_, knownMicrophone_); // double timeOfEvent = 25; // Event event12(timeOfEvent, 1, 0, 0); // Values values; // values.insert(key,event12); // double expectedTOA = timeOfEvent + 1 / Event::Speed; // EXPECT_DOUBLES_EQUAL(expectedTOA, expression.value(values), 1e-9); } //***************************************************************************** TEST(Event, Retract) { Event event, expected(1, 2, 3, 4); Vector4 v; v << 1, 2, 3, 4; EXPECT(assert_equal(expected, event.retract(v))); } //***************************************************************************** TEST( TOAFactor, Construct ) { Key key = 12; Expression event_(key); Point3 microphone; Expression knownMicrophone_(microphone); // constant expression double measurement = 7; TOAFactor factor(event_, knownMicrophone_, measurement, model); } //***************************************************************************** TEST( TOAFactor, WholeEnchilada ) { // Create microphones vector microphones; microphones.push_back(Point3(0, 0, 0)); microphones.push_back(Point3(403 * cm, 0, 0)); microphones.push_back(Point3(403 * cm, 403 * cm, 0)); microphones.push_back(Point3(0, 403 * cm, 0)); EXPECT_LONGS_EQUAL(4, microphones.size()); // Create a ground truth point const double timeOfEvent = 0; Event groundTruthEvent(timeOfEvent, 201.5 * cm, 201.5 * cm, (212 - 45) * cm); // Simulate measurements vector measurements(4); for (size_t i = 0; i < 4; i++) measurements[i] = groundTruthEvent.toa(microphones[i]); // Now, estimate using non-linear optimization NonlinearFactorGraph graph; Key key = 12; Expression event_(key); for (size_t i = 0; i < 4; i++) { Expression knownMicrophone_(microphones[i]); // constant expression graph.add(TOAFactor(event_, knownMicrophone_, measurements[i], model)); } /// Print the graph GTSAM_PRINT(graph); // Create initial estimate Values initialEstimate; Event estimatedEvent(timeOfEvent + 0.1, 200 * cm, 150 * cm, 50 * cm); initialEstimate.insert(key, estimatedEvent); // Print initialEstimate.print("Initial Estimate:\n"); // Optimize using Levenberg-Marquardt optimization. LevenbergMarquardtParams params; params.setVerbosity("ERROR"); LevenbergMarquardtOptimizer optimizer(graph, initialEstimate); Values result = optimizer.optimize(); result.print("Final Result:\n"); EXPECT(assert_equal(groundTruthEvent, result.at(key))); } //***************************************************************************** int main() { TestResult tr; return TestRegistry::runAllTests(tr); } //*****************************************************************************