/* ---------------------------------------------------------------------------- * 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 #include #include #include #include #include using namespace std; using namespace gtsam; // typedefs typedef Expression Point3_; typedef Expression Event_; // units static const double ms = 1e-3; static const double cm = 1e-2; // Create a noise model for the TOA error static SharedNoiseModel model(noiseModel::Isotropic::Sigma(1, 0.5 * ms)); static const double timeOfEvent = 25; static const Event exampleEvent(timeOfEvent, 1, 0, 0); static const Point3 sensorAt0(0, 0, 0); //***************************************************************************** TEST(TOAFactor, NewWay) { Key key = 12; double measurement = 7; TOAFactor factor(key, sensorAt0, measurement, model); } //***************************************************************************** TEST(TOAFactor, WholeEnchilada) { // Create sensors const double height = 0.5; vector sensors; sensors.push_back(Point3(0, 0, height)); sensors.push_back(Point3(403 * cm, 0, height)); sensors.push_back(Point3(403 * cm, 403 * cm, height)); sensors.push_back(Point3(0, 403 * cm, 2 * height)); EXPECT_LONGS_EQUAL(4, sensors.size()); // sensors.push_back(Point3(200 * cm, 200 * cm, height)); // Create a ground truth point const double timeOfEvent = 0; Event groundTruthEvent(timeOfEvent, 245 * cm, 201.5 * cm, (212 - 45) * cm); // Simulate simulatedTOA size_t K = sensors.size(); vector simulatedTOA(K); TimeOfArrival toa; for (size_t i = 0; i < K; i++) { simulatedTOA[i] = toa(groundTruthEvent, sensors[i]); } // Now, estimate using non-linear optimization NonlinearFactorGraph graph; Key key = 12; for (size_t i = 0; i < K; i++) { graph.emplace_shared(key, sensors[i], simulatedTOA[i], model); } // Create initial estimate Values initialEstimate; // Event estimatedEvent(timeOfEvent -10, 200 * cm, 150 * cm, 350 * cm); Vector4 delta; delta << 0.1, 0.1, -0.1, 0.1; Event estimatedEvent = groundTruthEvent.retract(delta); initialEstimate.insert(key, estimatedEvent); // Optimize using Levenberg-Marquardt optimization. LevenbergMarquardtParams params; params.setAbsoluteErrorTol(1e-10); LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, params); Values result = optimizer.optimize(); EXPECT(assert_equal(groundTruthEvent, result.at(key), 1e-6)); } //***************************************************************************** int main() { TestResult tr; return TestRegistry::runAllTests(tr); } //*****************************************************************************