148 lines
5.2 KiB
C++
148 lines
5.2 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file ImuFactorExample2
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* @brief Test example for using GTSAM ImuFactor and ImuCombinedFactor with ISAM2.
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* @author Robert Truax
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*/
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#include <gtsam/geometry/PinholeCamera.h>
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#include <gtsam/geometry/Cal3_S2.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/navigation/ImuBias.h>
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#include <gtsam/navigation/ImuFactor.h>
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#include <gtsam/navigation/Scenario.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <vector>
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using namespace std;
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using namespace gtsam;
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// Shorthand for velocity and pose variables
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using symbol_shorthand::V;
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using symbol_shorthand::X;
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const double kGravity = 9.81;
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/* ************************************************************************* */
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int main(int argc, char* argv[]) {
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auto params = PreintegrationParams::MakeSharedU(kGravity);
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params->setAccelerometerCovariance(I_3x3 * 0.1);
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params->setGyroscopeCovariance(I_3x3 * 0.1);
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params->setIntegrationCovariance(I_3x3 * 0.1);
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params->setUse2ndOrderCoriolis(false);
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params->setOmegaCoriolis(Vector3(0, 0, 0));
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Pose3 delta(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20));
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// Start with a camera on x-axis looking at origin
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double radius = 30;
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const Point3 up(0, 0, 1), target(0, 0, 0);
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const Point3 position(radius, 0, 0);
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const auto camera = PinholeCamera<Cal3_S2>::Lookat(position, target, up);
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const auto pose_0 = camera.pose();
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// Now, create a constant-twist scenario that makes the camera orbit the
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// origin
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double angular_velocity = M_PI, // rad/sec
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delta_t = 1.0 / 18; // makes for 10 degrees per step
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Vector3 angular_velocity_vector(0, -angular_velocity, 0);
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Vector3 linear_velocity_vector(radius * angular_velocity, 0, 0);
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auto scenario = ConstantTwistScenario(angular_velocity_vector,
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linear_velocity_vector, pose_0);
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// Create a factor graph
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NonlinearFactorGraph newgraph;
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// Create (incremental) ISAM2 solver
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ISAM2 isam;
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// Create the initial estimate to the solution
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// Intentionally initialize the variables off from the ground truth
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Values initialEstimate, totalEstimate, result;
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// Add a prior on pose x0. This indirectly specifies where the origin is.
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// 0.1 rad std on roll, pitch, yaw, 30cm std on x,y,z.
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auto noise = noiseModel::Diagonal::Sigmas(
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(Vector(6) << Vector3::Constant(0.1), Vector3::Constant(0.3)).finished());
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newgraph.addPrior(X(0), pose_0, noise);
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// Add imu priors
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Key biasKey = Symbol('b', 0);
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auto biasnoise = noiseModel::Diagonal::Sigmas(Vector6::Constant(0.1));
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newgraph.addPrior(biasKey, imuBias::ConstantBias(), biasnoise);
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initialEstimate.insert(biasKey, imuBias::ConstantBias());
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auto velnoise = noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1));
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Vector n_velocity(3);
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n_velocity << 0, angular_velocity * radius, 0;
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newgraph.addPrior(V(0), n_velocity, velnoise);
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initialEstimate.insert(V(0), n_velocity);
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// IMU preintegrator
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PreintegratedImuMeasurements accum(params);
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// Simulate poses and imu measurements, adding them to the factor graph
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for (size_t i = 0; i < 36; ++i) {
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double t = i * delta_t;
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if (i == 0) { // First time add two poses
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auto pose_1 = scenario.pose(delta_t);
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initialEstimate.insert(X(0), pose_0.compose(delta));
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initialEstimate.insert(X(1), pose_1.compose(delta));
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} else if (i >= 2) { // Add more poses as necessary
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auto pose_i = scenario.pose(t);
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initialEstimate.insert(X(i), pose_i.compose(delta));
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}
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if (i > 0) {
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// Add Bias variables periodically
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if (i % 5 == 0) {
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biasKey++;
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Symbol b1 = biasKey - 1;
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Symbol b2 = biasKey;
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Vector6 covvec;
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covvec << 0.1, 0.1, 0.1, 0.1, 0.1, 0.1;
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auto cov = noiseModel::Diagonal::Variances(covvec);
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auto f = std::make_shared<BetweenFactor<imuBias::ConstantBias> >(
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b1, b2, imuBias::ConstantBias(), cov);
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newgraph.add(f);
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initialEstimate.insert(biasKey, imuBias::ConstantBias());
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}
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// Predict acceleration and gyro measurements in (actual) body frame
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Vector3 measuredAcc = scenario.acceleration_b(t) -
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scenario.rotation(t).transpose() * params->n_gravity;
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Vector3 measuredOmega = scenario.omega_b(t);
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accum.integrateMeasurement(measuredAcc, measuredOmega, delta_t);
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// Add Imu Factor
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ImuFactor imufac(X(i - 1), V(i - 1), X(i), V(i), biasKey, accum);
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newgraph.add(imufac);
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// insert new velocity, which is wrong
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initialEstimate.insert(V(i), n_velocity);
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accum.resetIntegration();
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}
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// Incremental solution
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isam.update(newgraph, initialEstimate);
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result = isam.calculateEstimate();
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newgraph = NonlinearFactorGraph();
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initialEstimate.clear();
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}
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GTSAM_PRINT(result);
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return 0;
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}
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/* ************************************************************************* */
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