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demo_se3.cpp
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demo_se3.cpp
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/**
* \file demo_se3.cpp
*
* Created on: Feb 5, 2021
* \author: artivis
*
* ---------------------------------------------------------
* This file is:
* (c) 2021 artivis
*
* This file is part of `kalmanif`, a C++ template-only library
* for Kalman filtering on Lie groups targeted at estimation for robotics.
* Manif is:
* (c) 2015 mherb
* (c) 2021 artivis
* ---------------------------------------------------------
*
* ---------------------------------------------------------
* Demonstration example:
*
* 3D Robot localization based on fixed beacons.
*
* See demo_se2.cpp for the 2D equivalent.
* ---------------------------------------------------------
*
* This demo corresponds to the 3D version of the application
* in chapter V, section A, in the paper Sola-18,
* [https://arxiv.org/abs/1812.01537].
*
* The following is an abstract of the content of the paper.
* Please consult the paper for better reference.
*
*
* We consider a robot in 3D space surrounded by a small
* number of punctual landmarks or _beacons_.
* The robot receives control actions in the form of axial
* and angular velocities, and is able to measure the location
* of the beacons w.r.t its own reference frame.
*
* The robot pose X is in SE(3) and the beacon positions b_k in R^3,
*
* X = | R t | // position and orientation
* | 0 1 |
*
* b_k = (bx_k, by_k, bz_k) // lmk coordinates in world frame
*
* The control signal u is a twist in se(3) comprising longitudinal
* velocity vx and angular velocity wz, with no other velocity
* components, integrated over the sampling time dt.
*
* u = (vx*dt, 0, 0, 0, 0, w*dt)
*
* The control is corrupted by additive Gaussian noise u_noise,
* with covariance
*
* Q = diagonal(sigma_x^2, sigma_y^2, sigma_z^2, sigma_roll^2, sigma_pitch^2, sigma_yaw^2).
*
* This noise accounts for possible lateral and rotational slippage
* through a non-zero values of sigma_y, sigma_z, sigma_roll and sigma_pitch.
*
* At the arrival of a control u, the robot pose is updated
* with X <-- X * Exp(u) = X + u.
*
* Landmark measurements are of the range and bearing type,
* though they are put in Cartesian form for simplicity.
* Their noise n is zero mean Gaussian, and is specified
* with a covariances matrix R.
* We notice the rigid motion action y = h(X,b) = X^-1 * b
* (see appendix D),
*
* y_k = (brx_k, bry_k, brz_k) // lmk coordinates in robot frame
*
* We consider the beacons b_k situated at known positions.
* We define the pose to estimate as X in SE(3).
* The estimation error dx and its covariance P are expressed
* in the tangent space at X.
*
* All these variables are summarized again as follows
*
* X : robot pose, SE(3)
* u : robot control, (v*dt; 0; 0; 0; 0; w*dt) in se(3)
* Q : control perturbation covariance
* b_k : k-th landmark position, R^3
* y : Cartesian landmark measurement in robot frame, R^3
* R : covariance of the measurement noise
*
* The motion and measurement models are
*
* X_(t+1) = f(X_t, u) = X_t * Exp ( w ) // motion equation
* y_k = h(X, b_k) = X^-1 * b_k // measurement equation
*
* The algorithm below comprises first a simulator to
* produce measurements, then uses these measurements
* to estimate the state, using several Kalman filter available
* in the library.
*
* Printing simulated state and estimated state together
* with an unfiltered state (i.e. without Kalman corrections)
* allows for evaluating the quality of the estimates.
*/
#include <kalmanif/kalmanif.h>
#include "utils/rand.h"
#include "utils/plots.h"
#include "utils/utils.h"
#include <manif/SE3.h>
#include <vector>
using namespace kalmanif;
using namespace manif;
using State = SE3d;
using StateCovariance = Covariance<State>;
using SystemModel = LieSystemModel<State>;
using Control = SystemModel::Control;
using MeasurementModel = Landmark3DMeasurementModel<State>;
using Landmark = MeasurementModel::Landmark;
using Measurement = MeasurementModel::Measurement;
using Vector6d = Eigen::Matrix<double, 6, 1>;
using Array6d = Eigen::Array<double, 6, 1>;
using Matrix6d = Eigen::Matrix<double, 6, 6>;
// Filters
using EKF = ExtendedKalmanFilter<State>;
using SEKF = SquareRootExtendedKalmanFilter<State>;
using IEKF = InvariantExtendedKalmanFilter<State>;
using UKFM = UnscentedKalmanFilterManifolds<State>;
// Smoothers
using ERTS = RauchTungStriebelSmoother<EKF>;
using SERTS = RauchTungStriebelSmoother<SEKF>;
using IERTS = RauchTungStriebelSmoother<IEKF>;
using URTSM = RauchTungStriebelSmoother<UKFM>;
int main (int argc, char* argv[]) {
KALMANIF_DEMO_PROCESS_INPUT(argc, argv);
KALMANIF_DEMO_PRETTY_PRINT();
// START CONFIGURATION
constexpr double eot = 350; // s
constexpr double dt = 0.01; // s
double sqrtdt = std::sqrt(dt);
constexpr double var_gyro = 1e-4; // (rad/s)^2
constexpr double var_odometry = 9e-6; // (m/s)^2
constexpr int gps_freq = 10; // Hz
constexpr int landmark_freq = 50; // Hz
(void)landmark_freq;
(void)gps_freq;
State X_simulation = State::Identity(),
X_unfiltered = State::Identity(); // propagation only, for comparison purposes
// Define a control vector and its noise and covariance
Control u_simu, u_est, u_unfilt;
Vector6d u_nom, u_noisy, u_noise;
Array6d u_sigmas;
Matrix6d U;
u_nom << 0.1, 0.0, 0.05, 0.0, 0, 0.05;
u_sigmas.head<3>().setConstant(std::sqrt(var_odometry));
u_sigmas.tail<3>().setConstant(std::sqrt(var_gyro));
U = (u_sigmas * u_sigmas * 1./dt).matrix().asDiagonal();
// Define the beacon's measurements
Eigen::Vector3d y, y_noise;
Eigen::Array3d y_sigmas;
Eigen::Matrix3d R;
y_sigmas << 0.01, 0.01, 0.01;
R = (y_sigmas * y_sigmas).matrix().asDiagonal();
std::vector<MeasurementModel> measurement_models = {
MeasurementModel(Landmark(2.0, 0.0, 0.0), R),
MeasurementModel(Landmark(3.0, -1.0, -1.0), R),
MeasurementModel(Landmark(2.0, -1.0, 1.0), R),
MeasurementModel(Landmark(2.0, 1.0, 1.0), R),
MeasurementModel(Landmark(2.0, 1.0, -1.0), R)
};
std::vector<Measurement> measurements(measurement_models.size());
// Define the gps measurements
Eigen::Vector3d y_gps, y_gps_noise;
Eigen::Array3d y_gps_sigmas;
Eigen::Matrix3d R_gps;
y_gps_sigmas << std::sqrt(6e-3), std::sqrt(6e-3), std::sqrt(6e-3);
R_gps = (y_sigmas * y_sigmas).matrix().asDiagonal();
SystemModel system_model;
system_model.setCovariance(U);
StateCovariance state_cov_init = StateCovariance::Identity();
state_cov_init.bottomRightCorner<3,3>() *= MANIF_PI_4;
Vector6d n = randn<Array6d>();
Vector6d X_init_noise = state_cov_init.cwiseSqrt() * n;
State X_init = X_simulation + State::Tangent(X_init_noise);
EKF ekf;
ekf.setState(X_init);
ekf.setCovariance(state_cov_init);
SEKF sekf(X_init, state_cov_init);
IEKF iekf(X_init, state_cov_init);
UKFM ukfm(X_init, state_cov_init);
ERTS erts(X_init, state_cov_init);
SERTS serts(X_init, state_cov_init);
IERTS ierts(X_init, state_cov_init);
URTSM urtsm(X_init, state_cov_init);
// Store some data for plots
DemoDataCollector<State> collector;
collector.reserve(
eot/dt, "UNFI", "EKF", "SEKF", "IEKF", "UKFM", "ERTS", "SERTS", "IERTS", "URTSM"
);
// Make T steps. Measure up to K landmarks each time.
for (double t = 0; t < eot; t += dt) {
//// I. Simulation
/// simulate noise
u_noise = randn<Array6d>(u_sigmas / sqrtdt); // control noise
u_noisy = u_nom + u_noise; // noisy control
u_simu = u_nom * dt;
u_est = u_noisy * dt;
u_unfilt = u_noisy * dt;
/// first we move - - - - - - - - - - - - - - - - - - - - - - - - - - - -
X_simulation = system_model(X_simulation, u_simu);
/// then we measure all landmarks - - - - - - - - - - - - - - - - - - - -
for (std::size_t i = 0; i < measurement_models.size(); ++i) {
auto measurement_model = measurement_models[i];
y = measurement_model(X_simulation); // landmark measurement, before adding noise
/// simulate noise
y_noise = randn(y_sigmas); // measurement noise
y = y + y_noise; // landmark measurement, noisy
measurements[i] = y; // store for the estimator just below
}
//// II. Estimation
/// First we move
ekf.propagate(system_model, u_est);
sekf.propagate(system_model, u_est);
iekf.propagate(system_model, u_est, dt);
ukfm.propagate(system_model, u_est);
erts.propagate(system_model, u_est);
serts.propagate(system_model, u_est);
ierts.propagate(system_model, u_est, dt);
urtsm.propagate(system_model, u_est);
X_unfiltered = system_model(X_unfiltered, u_unfilt);
/// Then we correct using the measurements of each lmk
// if (int(t*100) % int(100./landmark_freq) == 0) {
for (std::size_t i = 0; i < measurement_models.size(); ++i) {
// landmark
auto measurement_model = measurement_models[i];
// measurement
y = measurements[i];
// filter update
ekf.update(measurement_model, y);
sekf.update(measurement_model, y);
iekf.update(measurement_model, y);
ukfm.update(measurement_model, y);
erts.update(measurement_model, y);
serts.update(measurement_model, y);
ierts.update(measurement_model, y);
urtsm.update(measurement_model, y);
}
// }
// if (int(t*100) % int(100./gps_freq) == 0) {
// gps measurement model
auto gps_measurement_model = DummyGPSMeasurementModel<State>(R_gps);
y_gps = gps_measurement_model(X_simulation); // gps measurement, before adding noise
/// simulate noise
y_gps_noise = randn(y_gps_sigmas); // measurement noise
y_gps = y_gps + y_gps_noise; // gps measurement, noisy
// filter update
ekf.update(gps_measurement_model, y_gps);
sekf.update(gps_measurement_model, y_gps);
iekf.update(gps_measurement_model, y_gps);
ukfm.update(gps_measurement_model, y_gps);
erts.update(gps_measurement_model, y_gps);
serts.update(gps_measurement_model, y_gps);
ierts.update(gps_measurement_model, y_gps);
urtsm.update(gps_measurement_model, y_gps);
// }
//// III. Results
collector.collect(X_simulation, t);
collector.collect("UNFI", X_unfiltered, StateCovariance::Zero(), t);
collector.collect("EKF", ekf.getState(), ekf.getCovariance(), t);
collector.collect("SEKF", sekf.getState(), sekf.getCovariance(), t);
collector.collect("IEKF", iekf.getState(), iekf.getCovariance(), t);
collector.collect("UKFM", ukfm.getState(), ukfm.getCovariance(), t);
}
// END OF TEMPORAL LOOP. DONE.
// Batch backward pass - smoothing
{
erts.smooth();
const auto& Xs_erts = erts.getStates();
const auto& Ps_erts = erts.getCovariances();
serts.smooth();
const auto& Xs_serts = serts.getStates();
const auto& Ps_serts = serts.getCovariances();
ierts.smooth();
const auto& Xs_ierts = ierts.getStates();
const auto& Ps_ierts = ierts.getCovariances();
urtsm.smooth();
const auto& Xs_urtsm = urtsm.getStates();
const auto& Ps_urtsm = urtsm.getCovariances();
// Collect smoothers' data
double t=0;
for (std::size_t i=0; i<Xs_erts.size(); ++i, t+=dt) {
collector.collect("ERTS", Xs_erts[i], Ps_erts[i], t);
collector.collect("SERTS", Xs_serts[i], Ps_serts[i], t);
collector.collect("IERTS", Xs_ierts[i], Ps_ierts[i], t);
collector.collect("URTSM", Xs_urtsm[i], Ps_urtsm[i], t);
}
}
// print the trajectory
if (!quiet) {
KALMANIF_DEMO_PRINT_TRAJECTORY(collector);
}
// Generate some metrics and print them
DemoDataProcessor<State>().process(collector).print();
// Actually plots only if PLOT_EXAMPLES=ON
DemoTrajPlotter<State>::plot(collector, filename, plot_trajectory);
DemoDataPlotter<State>::plot(collector, filename, plot_error);
return EXIT_SUCCESS;
}