Cleaned up test while reading it
dellaert
parent
0310eb4e7b
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18ff25b67f
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@ -1051,36 +1051,38 @@ TEST(ImuFactor, bodyPSensorNoBias) {
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imuBias::ConstantBias bias(Vector3(0, 0, 0), Vector3(0, 0.1, 0)); // Biases (acc, rot)
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// Measurements
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Vector3 n_gravity; n_gravity << 0, 0, -9.81; // z-up nav frame
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Vector3 omegaCoriolis; omegaCoriolis << 0, 0, 0;
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Vector3 n_gravity(0, 0, -9.81); // z-up nav frame
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Vector3 omegaCoriolis(0, 0, 0);
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// Sensor frame is z-down
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// Gyroscope measurement is the angular velocity of sensor w.r.t nav frame in sensor frame
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Vector3 s_omegaMeas_ns; s_omegaMeas_ns << 0, 0.1, M_PI/10;
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// Acc measurement is acceleration of sensor in the sensor frame, when stationary, table exerts an equal and opposite force
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// w.r.t gravity
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Vector3 s_accMeas; s_accMeas << 0,0,-9.81;
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Vector3 s_omegaMeas_ns(0, 0.1, M_PI / 10);
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// Acc measurement is acceleration of sensor in the sensor frame, when stationary,
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// table exerts an equal and opposite force w.r.t gravity
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Vector3 s_accMeas(0, 0, -9.81);
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double dt = 0.001;
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Pose3 b_P_s(Rot3::ypr(0,0,M_PI), Point3(0,0,0)); // Rotate sensor (z-down) to body (same as navigation) i.e. z-up
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Matrix I6x6(6,6);
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I6x6 = Matrix::Identity(6,6);
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// Rotate sensor (z-down) to body (same as navigation) i.e. z-up
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Pose3 body_P_sensor(Rot3::ypr(0, 0, M_PI), Point3(0, 0, 0));
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ImuFactor::PreintegratedMeasurements pre_int_data(bias, Matrix3::Zero(),
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Matrix3::Zero(), Matrix3::Zero(), true);
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ImuFactor::PreintegratedMeasurements pim(bias, Z_3x3, Z_3x3, Z_3x3, true);
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for (int i = 0; i<1000; ++i) pre_int_data.integrateMeasurement(s_accMeas, s_omegaMeas_ns, dt, b_P_s);
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for (int i = 0; i < 1000; ++i)
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pim.integrateMeasurement(s_accMeas, s_omegaMeas_ns, dt, body_P_sensor);
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// Create factor
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ImuFactor factor(X(1), V(1), X(2), V(2), B(1), pre_int_data, n_gravity, omegaCoriolis);
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// Create factor
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ImuFactor factor(X(1), V(1), X(2), V(2), B(1), pim, n_gravity, omegaCoriolis);
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// Predict
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Pose3 x1;
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Vector3 v1(0, 0.0, 0.0);
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PoseVelocityBias poseVelocity = pre_int_data.predict(x1, v1, bias, n_gravity, omegaCoriolis);
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Pose3 expectedPose(Rot3().ypr(-M_PI/10, 0, 0), Point3(0, 0, 0));
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Vector3 expectedVelocity; expectedVelocity<<0,0,0;
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EXPECT(assert_equal(expectedPose, poseVelocity.pose));
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EXPECT(assert_equal(Vector(expectedVelocity), Vector(poseVelocity.velocity)));
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// Predict
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Pose3 x1;
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Vector3 v1(0, 0, 0);
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PoseVelocityBias poseVelocity = pim.predict(x1, v1, bias, n_gravity,
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omegaCoriolis);
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Pose3 expectedPose(Rot3().ypr(-M_PI / 10, 0, 0), Point3(0, 0, 0));
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EXPECT(assert_equal(expectedPose, poseVelocity.pose));
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Vector3 expectedVelocity(0, 0, 0);
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EXPECT(assert_equal(Vector(expectedVelocity), Vector(poseVelocity.velocity)));
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}
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/* ************************************************************************* */
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@ -1091,22 +1093,27 @@ TEST(ImuFactor, bodyPSensorNoBias) {
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#include <gtsam/nonlinear/Marginals.h>
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TEST(ImuFactor, bodyPSensorWithBias) {
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using noiseModel::Diagonal;
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typedef imuBias::ConstantBias Bias;
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int numFactors = 80;
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Vector6 noiseBetweenBiasSigma; noiseBetweenBiasSigma << Vector3(2.0e-5, 2.0e-5, 2.0e-5), Vector3(3.0e-6, 3.0e-6, 3.0e-6);
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SharedDiagonal biasNoiseModel = noiseModel::Diagonal::Sigmas(noiseBetweenBiasSigma);
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Vector6 noiseBetweenBiasSigma;
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noiseBetweenBiasSigma << Vector3(2.0e-5, 2.0e-5, 2.0e-5), Vector3(3.0e-6,
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3.0e-6, 3.0e-6);
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SharedDiagonal biasNoiseModel = Diagonal::Sigmas(noiseBetweenBiasSigma);
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// Measurements
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Vector3 n_gravity; n_gravity << 0, 0, -9.81;
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Vector3 omegaCoriolis; omegaCoriolis << 0, 0, 0;
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Vector3 n_gravity(0, 0, -9.81);
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Vector3 omegaCoriolis(0, 0, 0);
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// Sensor frame is z-down
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// Gyroscope measurement is the angular velocity of sensor w.r.t nav frame in sensor frame
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Vector3 measuredOmega; measuredOmega << 0, 0.01, 0.0;
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// Acc measurement is acceleration of sensor in the sensor frame, when stationary, table exerts an equal and opposite force
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// w.r.t gravity
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Vector3 measuredAcc; measuredAcc << 0,0,-9.81;
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Vector3 measuredOmega(0, 0.01, 0);
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// Acc measurement is acceleration of sensor in the sensor frame, when stationary,
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// table exerts an equal and opposite force w.r.t gravity
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Vector3 measuredAcc(0, 0, -9.81);
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Pose3 bPs(Rot3::ypr(0,0,M_PI), Point3());
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Pose3 body_P_sensor(Rot3::ypr(0, 0, M_PI), Point3());
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Matrix3 accCov = 1e-7 * I_3x3;
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Matrix3 gyroCov = 1e-8 * I_3x3;
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@ -1114,18 +1121,19 @@ TEST(ImuFactor, bodyPSensorWithBias) {
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double deltaT = 0.005;
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// Specify noise values on priors
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Vector6 priorNoisePoseSigmas((Vector(6) << 0.001, 0.001, 0.001, 0.01, 0.01, 0.01).finished());
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Vector3 priorNoiseVelSigmas((Vector(3) << 0.1, 0.1, 0.1).finished());
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Vector6 priorNoiseBiasSigmas((Vector(6) << 0.1, 0.1, 0.1, 0.5e-1, 0.5e-1, 0.5e-1).finished());
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SharedDiagonal priorNoisePose = noiseModel::Diagonal::Sigmas(priorNoisePoseSigmas);
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SharedDiagonal priorNoiseVel = noiseModel::Diagonal::Sigmas(priorNoiseVelSigmas);
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SharedDiagonal priorNoiseBias = noiseModel::Diagonal::Sigmas(priorNoiseBiasSigmas);
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Vector3 zeroVel(0, 0.0, 0.0);
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Vector6 priorNoisePoseSigmas(
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(Vector(6) << 0.001, 0.001, 0.001, 0.01, 0.01, 0.01).finished());
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Vector3 priorNoiseVelSigmas((Vector(3) << 0.1, 0.1, 0.1).finished());
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Vector6 priorNoiseBiasSigmas(
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(Vector(6) << 0.1, 0.1, 0.1, 0.5e-1, 0.5e-1, 0.5e-1).finished());
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SharedDiagonal priorNoisePose = Diagonal::Sigmas(priorNoisePoseSigmas);
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SharedDiagonal priorNoiseVel = Diagonal::Sigmas(priorNoiseVelSigmas);
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SharedDiagonal priorNoiseBias = Diagonal::Sigmas(priorNoiseBiasSigmas);
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Vector3 zeroVel(0, 0, 0);
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Values values;
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// Create a factor graph with priors on initial pose, vlocity and bias
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NonlinearFactorGraph graph;
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Values values;
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PriorFactor<Pose3> priorPose(X(0), Pose3(), priorNoisePose);
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graph.add(priorPose);
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@ -1135,50 +1143,41 @@ TEST(ImuFactor, bodyPSensorWithBias) {
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graph.add(priorVel);
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values.insert(V(0), zeroVel);
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imuBias::ConstantBias priorBias(Vector3(0, 0, 0), Vector3(0.0, 0.01, 0)); // Biases (acc, rot)
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PriorFactor<imuBias::ConstantBias> priorBiasFactor(B(0), priorBias, priorNoiseBias);
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// The key to this test is that we specify the bias, in the sensor frame, as known a priori
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// We also create factors below that encode our assumption that this bias is constant over time
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// In theory, after optimization, we should recover that same bias estimate
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Bias priorBias(Vector3(0, 0, 0), Vector3(0, 0.01, 0)); // Biases (acc, rot)
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PriorFactor<Bias> priorBiasFactor(B(0), priorBias, priorNoiseBias);
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graph.add(priorBiasFactor);
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values.insert(B(0), priorBias);
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// Now add IMU factors and bias noise models
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Bias zeroBias(Vector3(0, 0, 0), Vector3(0, 0, 0));
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for (int i = 1; i < numFactors; i++) {
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ImuFactor::PreintegratedMeasurements pre_int_data = ImuFactor::PreintegratedMeasurements(imuBias::ConstantBias(Vector3(0, 0.0, 0.0),
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Vector3(0.0, 0.0, 0.0)), accCov, gyroCov, integrationCov, true);
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for (int j = 0; j<200; ++j) pre_int_data.integrateMeasurement(measuredAcc, measuredOmega, deltaT, bPs);
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ImuFactor::PreintegratedMeasurements pim =
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ImuFactor::PreintegratedMeasurements(zeroBias, accCov, gyroCov,
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integrationCov, true);
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for (int j = 0; j < 200; ++j)
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pim.integrateMeasurement(measuredAcc, measuredOmega, deltaT,
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body_P_sensor);
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// Create factor
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ImuFactor factor(X(i-1), V(i-1), X(i), V(i), B(i-1), pre_int_data, n_gravity, omegaCoriolis);
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graph.add(factor);
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imuBias::ConstantBias betweenBias(Vector3(0, 0, 0), Vector3(0.0, 0, 0));
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graph.add(BetweenFactor<imuBias::ConstantBias>(B(i-1), B(i), betweenBias, biasNoiseModel));
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// Create factors
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graph.add(
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ImuFactor(X(i - 1), V(i - 1), X(i), V(i), B(i - 1), pim, n_gravity,
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omegaCoriolis));
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graph.add(BetweenFactor<Bias>(B(i - 1), B(i), zeroBias, biasNoiseModel));
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values.insert(X(i), Pose3());
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values.insert(V(i), zeroVel);
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values.insert(B(i), priorBias);
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}
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// Finally, optimize, and get bias at last time step
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Values results = LevenbergMarquardtOptimizer(graph, values).optimize();
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cout.precision(2);
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Marginals marginals(graph, results);
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imuBias::ConstantBias biasActual = results.at<imuBias::ConstantBias>(B(numFactors-1));
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Bias biasActual = results.at<Bias>(B(numFactors - 1));
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results.print("Results: \n");
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for (int i = 0; i < numFactors; i++) {
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imuBias::ConstantBias currentBias = results.at<imuBias::ConstantBias>(B(i));
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cout << "currentBiasEstimate: " << currentBias.vector().transpose() << endl;
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}
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// for (int i = 0; i < numFactors; i++) {
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// Matrix biasCov = marginals.marginalCovariance(B(i));
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// cout << "b" << i << " cov: " << biasCov.diagonal().transpose() << endl;
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// }
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//
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for (int i = 0; i < numFactors; i++) {
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Pose3 currentPose = results.at<Pose3>(X(i));
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cout << "currentYPREstimate (in Deg): " << currentPose.rotation().ypr().transpose()*180/M_PI << endl;
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}
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// for (int i = 0; i < numFactors; i++)
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// cout << "x" << i << " covariance: " << marginals.marginalCovariance(X(i)).diagonal().transpose() << endl;
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imuBias::ConstantBias biasExpected(Vector3(0,0,0), Vector3(0, 0.01, 0.0));
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// And compare it with expected value (our prior)
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Bias biasExpected(Vector3(0, 0, 0), Vector3(0, 0.01, 0));
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EXPECT(assert_equal(biasExpected, biasActual, 1e-3));
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}
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