diff --git a/gtsam_unstable/slam/tests/testTOAFactor.cpp b/gtsam_unstable/slam/tests/testTOAFactor.cpp index 6ce248abe..072e0bcb9 100644 --- a/gtsam_unstable/slam/tests/testTOAFactor.cpp +++ b/gtsam_unstable/slam/tests/testTOAFactor.cpp @@ -17,8 +17,10 @@ * @date December 2014 */ +#include #include #include +#include namespace gtsam { @@ -38,11 +40,47 @@ public: time_(0) { } - /// Constructor + /// 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 { @@ -87,7 +125,7 @@ public: }; -}//\ namespace gtsam +} //\ namespace gtsam #include #include @@ -98,9 +136,10 @@ using namespace std; using namespace gtsam; // Create a noise model for the TOA error -static const double ms = 1e-3, cm = 1e-2; +//static const double ms = 1e-3; +static const double cm = 1e-2; typedef Eigen::Matrix Vector1; -static SharedNoiseModel model(noiseModel::Diagonal::Sigmas(Vector1(5. * ms))); +static SharedNoiseModel model(noiseModel::Unit::Create(1)); //***************************************************************************** TEST( Event, Constructor ) { @@ -109,7 +148,7 @@ TEST( Event, Constructor ) { } //***************************************************************************** -TEST( TOA, Toa1 ) { +TEST( Event, Toa1 ) { Point3 microphone; Event event(0, 1, 0, 0); double expected = 1 / Event::Speed; @@ -117,7 +156,7 @@ TEST( TOA, Toa1 ) { } //***************************************************************************** -TEST( TOA, Toa2 ) { +TEST( Event, Toa2 ) { Point3 microphone; double timeOfEvent = 25; Event event(timeOfEvent, 1, 0, 0); @@ -126,7 +165,7 @@ TEST( TOA, Toa2 ) { } //***************************************************************************** -TEST( TOA, Expression ) { +TEST( Event, Expression ) { Key key = 12; Expression event_(key); Point3 microphone; @@ -142,7 +181,15 @@ TEST( TOA, Expression ) { } //***************************************************************************** -TEST( TOAFactor, Constract ) { +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; @@ -164,12 +211,12 @@ TEST( TOAFactor, WholeEnchilada ) { // Create a ground truth point const double timeOfEvent = 0; - Event event(timeOfEvent, 201.5 * cm, 201.5 * cm, (212 - 45) * cm); + 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] = event.toa(microphones[i]); + measurements[i] = groundTruthEvent.toa(microphones[i]); // Now, estimate using non-linear optimization NonlinearFactorGraph graph; @@ -179,6 +226,25 @@ TEST( TOAFactor, WholeEnchilada ) { 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))); } //*****************************************************************************