Fixed both C++ and python examples
Frank Dellaert
parent
1d214d4529
commit
264a240094
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@ -31,24 +31,6 @@ def vector3(x, y, z):
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return np.array([x, y, z], dtype=np.float)
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def create_poses(angular_velocity=np.pi,
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delta_t=0.01, radius=30.0):
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"""Create the set of ground-truth poses."""
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poses = []
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theta = 0.0
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up = gtsam.Point3(0, 0, 1)
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target = gtsam.Point3(0, 0, 0)
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for i in range(80):
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position = gtsam.Point3(radius * math.cos(theta),
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radius * math.sin(theta), 0.0)
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camera = gtsam.SimpleCamera.Lookat(
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position, target, up, gtsam.Cal3_S2())
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poses.append(camera.pose())
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theta += delta_t * angular_velocity
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return poses
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def ISAM2_plot(values, fignum=0):
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"""Plot poses."""
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fig = plt.figure(fignum)
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@ -77,8 +59,9 @@ def ISAM2_plot(values, fignum=0):
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# IMU preintegration parameters
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# Default Params for a Z-up navigation frame, such as ENU: gravity points along negative Z-axis
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g = 9.81
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I = np.eye(3)
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n_gravity = vector3(0, 0, -g)
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PARAMS = gtsam.PreintegrationParams.MakeSharedU(g)
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I = np.eye(3)
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PARAMS.setAccelerometerCovariance(I * 0.1)
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PARAMS.setGyroscopeCovariance(I * 0.1)
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PARAMS.setIntegrationCovariance(I * 0.1)
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@ -86,16 +69,29 @@ PARAMS.setUse2ndOrderCoriolis(False)
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PARAMS.setOmegaCoriolis(vector3(0, 0, 0))
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BIAS_COVARIANCE = gtsam.noiseModel_Isotropic.Variance(6, 0.1)
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DELTA = gtsam.Pose3(gtsam.Rot3.Rodrigues(0, 0, 0),
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gtsam.Point3(0.05, -0.10, 0.20))
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def IMU_example():
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"""Run iSAM 2 example with IMU factor."""
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# Start with a camera on x-axis looking at origin
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radius = 30
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up = gtsam.Point3(0, 0, 1)
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target = gtsam.Point3(0, 0, 0)
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position = gtsam.Point3(radius, 0, 0)
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camera = gtsam.SimpleCamera.Lookat(position, target, up, gtsam.Cal3_S2())
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pose_0 = camera.pose()
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# Create the set of ground-truth landmarks and poses
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angular_velocity = math.radians(180) # rad/sec
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delta_t = 1.0/18 # makes for 10 degrees per step
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radius = 30
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acceleration = radius * angular_velocity**2
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poses = create_poses(angular_velocity, delta_t, radius)
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angular_velocity_vector = vector3(0, -angular_velocity, 0)
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linear_velocity_vector = vector3(radius * angular_velocity, 0, 0)
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scenario = gtsam.ConstantTwistScenario(
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angular_velocity_vector, linear_velocity_vector, pose_0)
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# Create a factor graph
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newgraph = gtsam.NonlinearFactorGraph()
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@ -111,7 +107,7 @@ def IMU_example():
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# 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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noise = gtsam.noiseModel_Diagonal.Sigmas(
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np.array([0.3, 0.3, 0.3, 0.1, 0.1, 0.1]))
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newgraph.push_back(gtsam.PriorFactorPose3(X(0), poses[0], noise))
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newgraph.push_back(gtsam.PriorFactorPose3(X(0), pose_0, noise))
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# Add imu priors
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biasKey = gtsam.symbol(ord('b'), 0)
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@ -123,22 +119,23 @@ def IMU_example():
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velnoise = gtsam.noiseModel_Isotropic.Sigma(3, 0.1)
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# Calculate with correct initial velocity
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velocity = vector3(0, angular_velocity * radius, 0)
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velprior = gtsam.PriorFactorVector(V(0), velocity, velnoise)
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n_velocity = vector3(0, angular_velocity * radius, 0)
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velprior = gtsam.PriorFactorVector(V(0), n_velocity, velnoise)
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newgraph.push_back(velprior)
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initialEstimate.insert(V(0), velocity)
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initialEstimate.insert(V(0), n_velocity)
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accum = gtsam.PreintegratedImuMeasurements(PARAMS)
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# Simulate poses and imu measurements, adding them to the factor graph
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for i, pose_i in enumerate(poses):
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delta = gtsam.Pose3(gtsam.Rot3.Rodrigues(0, 0, 0),
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gtsam.Point3(0.05, -0.10, 0.20))
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for i in range(80):
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t = i * delta_t # simulation time
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if i == 0: # First time add two poses
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initialEstimate.insert(X(0), poses[0].compose(delta))
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initialEstimate.insert(X(1), poses[1].compose(delta))
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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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elif i >= 2: # Add more poses as necessary
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initialEstimate.insert(X(i), pose_i.compose(delta))
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pose_i = scenario.pose(t)
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initialEstimate.insert(X(i), pose_i.compose(DELTA))
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if i > 0:
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# Add Bias variables periodically
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@ -150,11 +147,10 @@ def IMU_example():
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initialEstimate.insert(biasKey, gtsam.imuBias_ConstantBias())
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# Predict acceleration and gyro measurements in (actual) body frame
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nRb = pose_i.rotation().matrix()
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nRb = scenario.rotation(t).matrix()
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bRn = np.transpose(nRb)
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measuredAcc = - np.dot(bRn, vector3(0, 0, -g)) + \
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vector3(0, 0, acceleration) # in body
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measuredOmega = np.dot(bRn, vector3(0, 0, angular_velocity))
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measuredAcc = scenario.acceleration_b(t) - np.dot(bRn, n_gravity)
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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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@ -162,8 +158,8 @@ def IMU_example():
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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
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initialEstimate.insert(V(i), velocity)
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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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# Incremental solution
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@ -3,42 +3,51 @@
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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 <gtsam/slam/PriorFactor.h>
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#include <vector>
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#define GTSAM4
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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vector<Pose3> createPoses() {
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// Create the set of ground-truth poses
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vector<Pose3> poses;
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double radius = 30.0, theta = 0.0;
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Point3 up(0, 0, 1), target(0, 0, 0);
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for (size_t i = 0; i < 80; ++i, theta += 2 * M_PI / 8) {
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Point3 position(radius * cos(theta), radius * sin(theta), 0.0);
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SimpleCamera camera = SimpleCamera::Lookat(position, target, up);
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poses.push_back(camera.pose());
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}
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return poses;
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}
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/* ************************************************************************* */
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int main(int argc, char* argv[]) {
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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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// Create the set of ground-truth landmarks and poses
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vector<Pose3> poses = createPoses();
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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 SimpleCamera camera = SimpleCamera::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, totalgraph;
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NonlinearFactorGraph newgraph;
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// Create (incremental) ISAM2 solver
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ISAM2 isam;
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@ -51,8 +60,7 @@ int main(int argc, char* argv[]) {
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// 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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auto noise = noiseModel::Diagonal::Sigmas(
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(Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1)).finished());
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newgraph.push_back(PriorFactor<Pose3>(X(0), poses[0], noise));
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totalgraph.push_back(PriorFactor<Pose3>(X(0), poses[0], noise));
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newgraph.push_back(PriorFactor<Pose3>(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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@ -60,65 +68,29 @@ int main(int argc, char* argv[]) {
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PriorFactor<imuBias::ConstantBias> biasprior(biasKey, imuBias::ConstantBias(),
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biasnoise);
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newgraph.push_back(biasprior);
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totalgraph.push_back(biasprior);
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initialEstimate.insert(biasKey, imuBias::ConstantBias());
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totalEstimate.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 gravity(3), zero(3), velocity(3);
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gravity << 0, 0, -9.8;
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zero << 0, 0, 0;
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velocity << 0, 0, 0;
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#ifdef GTSAM4
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PriorFactor<Vector> velprior(V(0), zero, velnoise);
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#else
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PriorFactor<LieVector> velprior(V(0), zero, velnoise);
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#endif
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Vector n_velocity(3);
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n_velocity << 0, angular_velocity * radius, 0;
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PriorFactor<Vector> velprior(V(0), n_velocity, velnoise);
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newgraph.push_back(velprior);
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totalgraph.push_back(velprior);
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#ifdef GTSAM4
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initialEstimate.insert(V(0), zero);
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totalEstimate.insert(V(0), zero);
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#else
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initialEstimate.insert(V(0), LieVector(zero));
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totalEstimate.insert(V(0), LieVector(zero));
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#endif
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initialEstimate.insert(V(0), n_velocity);
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Matrix3 I;
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I << 1, 0, 0, 0, 1, 0, 0, 0, 1;
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Matrix3 accCov = I * 0.1;
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Matrix3 gyroCov = I * 0.1;
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Matrix3 intCov = I * 0.1;
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bool secOrder = false;
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#ifdef GTSAM4
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// IMU preintegrator
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PreintegratedImuMeasurements accum(PreintegrationParams::MakeSharedD());
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accum.params()->setAccelerometerCovariance(accCov);
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accum.params()->setGyroscopeCovariance(gyroCov);
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accum.params()->setIntegrationCovariance(intCov);
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accum.params()->setUse2ndOrderCoriolis(secOrder);
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accum.params()->setOmegaCoriolis(Vector3(0, 0, 0));
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#else
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ImuFactor::PreintegratedMeasurements accum(imuBias::ConstantBias(), accCov,
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gyroCov, intCov, secOrder);
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#endif
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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 < poses.size(); ++i) {
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#ifdef GTSAM4
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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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#else
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Pose3 delta(Rot3::ypr(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20));
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#endif
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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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initialEstimate.insert(X(0), poses[0].compose(delta));
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initialEstimate.insert(X(1), poses[1].compose(delta));
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totalEstimate.insert(X(0), poses[0].compose(delta));
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totalEstimate.insert(X(1), poses[1].compose(delta));
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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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initialEstimate.insert(X(i), poses[i].compose(delta));
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totalEstimate.insert(X(i), poses[i].compose(delta));
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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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@ -135,44 +107,30 @@ int main(int argc, char* argv[]) {
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auto f = boost::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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totalgraph.add(f);
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initialEstimate.insert(biasKey, imuBias::ConstantBias());
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totalEstimate.insert(biasKey, imuBias::ConstantBias());
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}
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// Add Imu Factor
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accum.integrateMeasurement(gravity, zero, 0.5);
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#ifdef GTSAM4
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ImuFactor imufac(X(i - 1), V(i - 1), X(i), V(i), biasKey, accum);
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#else
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ImuFactor imufac(X(i - 1), V(i - 1), X(i), V(i), biasKey, accum, gravity,
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zero);
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#endif
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newgraph.add(imufac);
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totalgraph.add(imufac);
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// Predict acceleration and gyro measurements in (actual) body frame
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auto measuredAcc = scenario.acceleration_b(t) -
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scenario.rotation(t).transpose() * params->n_gravity;
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auto measuredOmega = scenario.omega_b(t);
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accum.integrateMeasurement(measuredAcc, measuredOmega, delta_t);
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// insert new velocity
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#ifdef GTSAM4
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initialEstimate.insert(V(i), velocity);
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totalEstimate.insert(V(i), velocity);
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#else
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initialEstimate.insert(V(i), LieVector(velocity));
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totalEstimate.insert(V(i), LieVector(velocity));
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#endif
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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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// Batch solution
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ISAM2 isam_full;
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isam_full.update(totalgraph, totalEstimate);
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result = isam_full.calculateEstimate();
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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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