138 lines
4.9 KiB
C++
138 lines
4.9 KiB
C++
/* ----------------------------------------------------------------------------
|
|
|
|
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
|
* Atlanta, Georgia 30332-0415
|
|
* All Rights Reserved
|
|
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
|
|
|
* See LICENSE for the license information
|
|
|
|
* -------------------------------------------------------------------------- */
|
|
|
|
/**
|
|
* @file PlanarSLAMSelfContained_advanced.cpp
|
|
* @brief Simple robotics example with all typedefs internal to this script.
|
|
* @author Alex Cunningham
|
|
*/
|
|
|
|
#include <cmath>
|
|
#include <iostream>
|
|
|
|
// for all nonlinear keys
|
|
#include <gtsam/nonlinear/Symbol.h>
|
|
|
|
// for points and poses
|
|
#include <gtsam/geometry/Point2.h>
|
|
#include <gtsam/geometry/Pose2.h>
|
|
|
|
// for modeling measurement uncertainty - all models included here
|
|
#include <gtsam/linear/NoiseModel.h>
|
|
|
|
// add in headers for specific factors
|
|
#include <gtsam/slam/PriorFactor.h>
|
|
#include <gtsam/slam/BetweenFactor.h>
|
|
#include <gtsam/slam/BearingRangeFactor.h>
|
|
|
|
// implementations for structures - needed if self-contained, and these should be included last
|
|
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
|
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
|
|
#include <gtsam/nonlinear/Marginals.h>
|
|
|
|
using namespace std;
|
|
using namespace gtsam;
|
|
|
|
/**
|
|
* In this version of the system we make the following assumptions:
|
|
* - All values are axis aligned
|
|
* - Robot poses are facing along the X axis (horizontal, to the right in images)
|
|
* - We have bearing and range information for measurements
|
|
* - We have full odometry for measurements
|
|
* - The robot and landmarks are on a grid, moving 2 meters each step
|
|
* - Landmarks are 2 meters away from the robot trajectory
|
|
*/
|
|
int main(int argc, char** argv) {
|
|
// create keys for variables
|
|
Symbol x1('x',1), x2('x',2), x3('x',3);
|
|
Symbol l1('l',1), l2('l',2);
|
|
|
|
// create graph container and add factors to it
|
|
NonlinearFactorGraph graph;
|
|
|
|
/* add prior */
|
|
// gaussian for prior
|
|
SharedDiagonal prior_model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
|
|
Pose2 prior_measurement(0.0, 0.0, 0.0); // prior at origin
|
|
PriorFactor<Pose2> posePrior(x1, prior_measurement, prior_model); // create the factor
|
|
graph.add(posePrior); // add the factor to the graph
|
|
|
|
/* add odometry */
|
|
// general noisemodel for odometry
|
|
SharedDiagonal odom_model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
|
|
Pose2 odom_measurement(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
|
|
// create between factors to represent odometry
|
|
BetweenFactor<Pose2> odom12(x1, x2, odom_measurement, odom_model);
|
|
BetweenFactor<Pose2> odom23(x2, x3, odom_measurement, odom_model);
|
|
graph.add(odom12); // add both to graph
|
|
graph.add(odom23);
|
|
|
|
/* add measurements */
|
|
// general noisemodel for measurements
|
|
SharedDiagonal meas_model = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.2));
|
|
|
|
// create the measurement values - indices are (pose id, landmark id)
|
|
Rot2 bearing11 = Rot2::fromDegrees(45),
|
|
bearing21 = Rot2::fromDegrees(90),
|
|
bearing32 = Rot2::fromDegrees(90);
|
|
double range11 = sqrt(4+4),
|
|
range21 = 2.0,
|
|
range32 = 2.0;
|
|
|
|
// create bearing/range factors
|
|
BearingRangeFactor<Pose2, Point2> meas11(x1, l1, bearing11, range11, meas_model);
|
|
BearingRangeFactor<Pose2, Point2> meas21(x2, l1, bearing21, range21, meas_model);
|
|
BearingRangeFactor<Pose2, Point2> meas32(x3, l2, bearing32, range32, meas_model);
|
|
|
|
// add the factors
|
|
graph.add(meas11);
|
|
graph.add(meas21);
|
|
graph.add(meas32);
|
|
|
|
graph.print("Full Graph");
|
|
|
|
// initialize to noisy points
|
|
Values initial;
|
|
initial.insert(x1, Pose2(0.5, 0.0, 0.2));
|
|
initial.insert(x2, Pose2(2.3, 0.1,-0.2));
|
|
initial.insert(x3, Pose2(4.1, 0.1, 0.1));
|
|
initial.insert(l1, Point2(1.8, 2.1));
|
|
initial.insert(l2, Point2(4.1, 1.8));
|
|
|
|
initial.print("initial estimate");
|
|
|
|
// optimize using Levenberg-Marquardt optimization with an ordering from colamd
|
|
|
|
// first using sequential elimination
|
|
LevenbergMarquardtParams lmParams;
|
|
lmParams.elimination = LevenbergMarquardtParams::SEQUENTIAL;
|
|
Values resultSequential = LevenbergMarquardtOptimizer(graph, initial, lmParams).optimize();
|
|
resultSequential.print("final result (solved with a sequential solver)");
|
|
|
|
// then using multifrontal, advanced interface
|
|
// Note that we keep the original optimizer object so we can use the COLAMD
|
|
// ordering it computes.
|
|
LevenbergMarquardtOptimizer optimizer(graph, initial);
|
|
Values resultMultifrontal = optimizer.optimize();
|
|
resultMultifrontal.print("final result (solved with a multifrontal solver)");
|
|
|
|
// Print marginals covariances for all variables
|
|
Marginals marginals(graph, resultMultifrontal, Marginals::CHOLESKY);
|
|
print(marginals.marginalCovariance(x1), "x1 covariance");
|
|
print(marginals.marginalCovariance(x2), "x2 covariance");
|
|
print(marginals.marginalCovariance(x3), "x3 covariance");
|
|
print(marginals.marginalCovariance(l1), "l1 covariance");
|
|
print(marginals.marginalCovariance(l2), "l2 covariance");
|
|
|
|
return 0;
|
|
}
|
|
|