180 lines
		
	
	
		
			6.5 KiB
		
	
	
	
		
			C++
		
	
	
			
		
		
	
	
			180 lines
		
	
	
		
			6.5 KiB
		
	
	
	
		
			C++
		
	
	
| /**
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|  * @file testIMUSystem
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|  * @author Alex Cunningham
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|  */
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| 
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| #include <iostream>
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| 
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| #include <CppUnitLite/TestHarness.h>
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| 
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| #include <gtsam/slam/BetweenFactor.h>
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| #include <gtsam/slam/PriorFactor.h>
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| #include <gtsam/slam/RangeFactor.h>
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| #include <gtsam_unstable/slam/PartialPriorFactor.h>
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| #include <gtsam/nonlinear/NonlinearEquality.h>
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| #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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| #include <gtsam/nonlinear/NonlinearFactorGraph.h>
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| 
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| #include <gtsam_unstable/dynamics/IMUFactor.h>
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| #include <gtsam_unstable/dynamics/FullIMUFactor.h>
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| #include <gtsam_unstable/dynamics/VelocityConstraint.h>
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| #include <gtsam_unstable/dynamics/DynamicsPriors.h>
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| 
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| using namespace std;
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| using namespace gtsam;
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| 
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| const double tol=1e-5;
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| 
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| static const Key x0 = 0, x1 = 1, x2 = 2, x3 = 3, x4 = 4;
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| static const Vector g = delta(3, 2, -9.81);
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| 
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| /* ************************************************************************* */
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| TEST(testIMUSystem, instantiations) {
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|   // just checking for compilation
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|   PoseRTV x1_v;
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| 
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|   gtsam::SharedNoiseModel model1 = gtsam::noiseModel::Unit::Create(1);
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|   gtsam::SharedNoiseModel model3 = gtsam::noiseModel::Unit::Create(3);
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|   gtsam::SharedNoiseModel model6 = gtsam::noiseModel::Unit::Create(6);
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|   gtsam::SharedNoiseModel model9 = gtsam::noiseModel::Unit::Create(9);
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| 
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|   Vector accel = ones(3), gyro = ones(3);
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| 
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|   IMUFactor<PoseRTV> imu(accel, gyro, 0.01, x1, x2, model6);
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|   FullIMUFactor<PoseRTV> full_imu(accel, gyro, 0.01, x1, x2, model9);
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|   NonlinearEquality<gtsam::PoseRTV> poseHardPrior(x1, x1_v);
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|   BetweenFactor<gtsam::PoseRTV> odom(x1, x2, x1_v, model9);
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|   RangeFactor<gtsam::PoseRTV, gtsam::PoseRTV> range(x1, x2, 1.0, model1);
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|   VelocityConstraint constraint(x1, x2, 0.1, 10000);
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|   PriorFactor<gtsam::PoseRTV> posePrior(x1, x1_v, model9);
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|   DHeightPrior heightPrior(x1, 0.1, model1);
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|   VelocityPrior velPrior(x1, ones(3), model3);
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| }
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| 
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| /* ************************************************************************* */
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| TEST( testIMUSystem, optimize_chain ) {
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|   // create a simple chain of poses to generate IMU measurements
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|   const double dt = 1.0;
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|   PoseRTV pose1,
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|           pose2(Point3(1.0, 1.0, 0.0), Rot3::ypr(0.1, 0.0, 0.0), Vector_(3, 2.0, 2.0, 0.0)),
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|           pose3(Point3(2.0, 2.0, 0.0), Rot3::ypr(0.2, 0.0, 0.0), Vector_(3, 0.0, 0.0, 0.0)),
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|           pose4(Point3(3.0, 3.0, 0.0), Rot3::ypr(0.3, 0.0, 0.0), Vector_(3, 2.0, 2.0, 0.0));
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| 
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|   // create measurements
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|   SharedDiagonal model = noiseModel::Unit::Create(6);
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|   Vector imu12(6), imu23(6), imu34(6);
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|   imu12 = pose1.imuPrediction(pose2, dt);
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|   imu23 = pose2.imuPrediction(pose3, dt);
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|   imu34 = pose3.imuPrediction(pose4, dt);
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| 
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|   // assemble simple graph with IMU measurements and velocity constraints
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|   NonlinearFactorGraph graph;
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|   graph.add(NonlinearEquality<gtsam::PoseRTV>(x1, pose1));
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|   graph.add(IMUFactor<PoseRTV>(imu12, dt, x1, x2, model));
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|   graph.add(IMUFactor<PoseRTV>(imu23, dt, x2, x3, model));
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|   graph.add(IMUFactor<PoseRTV>(imu34, dt, x3, x4, model));
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|   graph.add(VelocityConstraint(x1, x2, dt));
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|   graph.add(VelocityConstraint(x2, x3, dt));
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|   graph.add(VelocityConstraint(x3, x4, dt));
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| 
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|   // ground truth values
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|   Values true_values;
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|   true_values.insert(x1, pose1);
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|   true_values.insert(x2, pose2);
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|   true_values.insert(x3, pose3);
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|   true_values.insert(x4, pose4);
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| 
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|   // verify zero error
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|   EXPECT_DOUBLES_EQUAL(0, graph.error(true_values), 1e-5);
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| 
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|   // initialize with zero values and optimize
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|   Values values;
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|   values.insert(x1, PoseRTV());
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|   values.insert(x2, PoseRTV());
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|   values.insert(x3, PoseRTV());
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|   values.insert(x4, PoseRTV());
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| 
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|   Values actual = LevenbergMarquardtOptimizer(graph, values).optimize();
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|   EXPECT(assert_equal(true_values, actual, tol));
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| }
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| 
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| /* ************************************************************************* */
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| TEST( testIMUSystem, optimize_chain_fullfactor ) {
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|   // create a simple chain of poses to generate IMU measurements
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|   const double dt = 1.0;
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|   PoseRTV pose1,
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|           pose2(Point3(1.0, 0.0, 0.0), Rot3::ypr(0.0, 0.0, 0.0), Vector_(3, 1.0, 0.0, 0.0)),
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|           pose3(Point3(2.0, 0.0, 0.0), Rot3::ypr(0.0, 0.0, 0.0), Vector_(3, 1.0, 0.0, 0.0)),
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|           pose4(Point3(3.0, 0.0, 0.0), Rot3::ypr(0.0, 0.0, 0.0), Vector_(3, 1.0, 0.0, 0.0));
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| 
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|   // create measurements
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|   SharedDiagonal model = noiseModel::Isotropic::Sigma(9, 1.0);
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|   Vector imu12(6), imu23(6), imu34(6);
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|   imu12 = pose1.imuPrediction(pose2, dt);
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|   imu23 = pose2.imuPrediction(pose3, dt);
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|   imu34 = pose3.imuPrediction(pose4, dt);
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| 
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|   // assemble simple graph with IMU measurements and velocity constraints
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|   NonlinearFactorGraph graph;
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|   graph.add(NonlinearEquality<gtsam::PoseRTV>(x1, pose1));
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|   graph.add(FullIMUFactor<PoseRTV>(imu12, dt, x1, x2, model));
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|   graph.add(FullIMUFactor<PoseRTV>(imu23, dt, x2, x3, model));
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|   graph.add(FullIMUFactor<PoseRTV>(imu34, dt, x3, x4, model));
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| 
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|   // ground truth values
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|   Values true_values;
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|   true_values.insert(x1, pose1);
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|   true_values.insert(x2, pose2);
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|   true_values.insert(x3, pose3);
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|   true_values.insert(x4, pose4);
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| 
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|   // verify zero error
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|   EXPECT_DOUBLES_EQUAL(0, graph.error(true_values), 1e-5);
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| 
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|   // initialize with zero values and optimize
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|   Values values;
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|   values.insert(x1, PoseRTV());
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|   values.insert(x2, PoseRTV());
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|   values.insert(x3, PoseRTV());
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|   values.insert(x4, PoseRTV());
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| 
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|   cout << "Initial Error: " << graph.error(values) << endl; // Initial error is 0.5 - need better prediction model
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| 
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|   Values actual = LevenbergMarquardtOptimizer(graph, values).optimize();
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| //  EXPECT(assert_equal(true_values, actual, tol)); // FAIL
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| }
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| 
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| /* ************************************************************************* */
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| TEST( testIMUSystem, linear_trajectory) {
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|   // create a linear trajectory of poses
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|   // and verify simple solution
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|   const double dt = 1.0;
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| 
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|   PoseRTV start;
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|   Vector accel = delta(3, 0, 0.5); // forward force
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|   Vector gyro = delta(3, 0, 0.1); // constant rotation
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|   SharedDiagonal model = noiseModel::Unit::Create(9);
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| 
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|   Values true_traj, init_traj;
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|   NonlinearFactorGraph graph;
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| 
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|   graph.add(NonlinearEquality<gtsam::PoseRTV>(x0, start));
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|   true_traj.insert(x0, start);
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|   init_traj.insert(x0, start);
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| 
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|   size_t nrPoses = 10;
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|   PoseRTV cur_pose = start;
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|   for (size_t i=1; i<nrPoses; ++i) {
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|     Key xA = i-1, xB = i;
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|     cur_pose = cur_pose.generalDynamics(accel, gyro, dt);
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|     graph.add(FullIMUFactor<PoseRTV>(accel - g, gyro, dt, xA, xB, model));
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|     true_traj.insert(xB, cur_pose);
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|     init_traj.insert(xB, PoseRTV());
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|   }
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| //  EXPECT_DOUBLES_EQUAL(0, graph.error(true_traj), 1e-5); // FAIL
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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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