138 lines
4.9 KiB
C++
138 lines
4.9 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 PlanarSLAMExample_selfcontained.cpp
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* @brief Simple robotics example with all typedefs internal to this script.
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* @author Alex Cunningham
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*/
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// add in headers for specific factors
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/slam/BearingRangeFactor.h>
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// for all nonlinear keys
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#include <gtsam/nonlinear/Symbol.h>
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// implementations for structures - needed if self-contained, and these should be included last
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/Marginals.h>
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// for modeling measurement uncertainty - all models included here
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#include <gtsam/linear/NoiseModel.h>
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// for points and poses
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#include <gtsam/geometry/Point2.h>
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#include <gtsam/geometry/Pose2.h>
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#include <cmath>
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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/**
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* In this version of the system we make the following assumptions:
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* - All values are axis aligned
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* - Robot poses are facing along the X axis (horizontal, to the right in images)
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* - We have bearing and range information for measurements
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* - We have full odometry for measurements
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* - The robot and landmarks are on a grid, moving 2 meters each step
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* - Landmarks are 2 meters away from the robot trajectory
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*/
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int main(int argc, char** argv) {
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// create keys for variables
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Symbol i1('x',1), i2('x',2), i3('x',3);
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Symbol j1('l',1), j2('l',2);
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// create graph container and add factors to it
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NonlinearFactorGraph graph;
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/* add prior */
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// gaussian for prior
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SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
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PriorFactor<Pose2> posePrior(i1, priorMean, priorNoise); // create the factor
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graph.add(posePrior); // add the factor to the graph
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/* add odometry */
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// general noisemodel for odometry
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SharedDiagonal odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
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// create between factors to represent odometry
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BetweenFactor<Pose2> odom12(i1, i2, odometry, odometryNoise);
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BetweenFactor<Pose2> odom23(i2, i3, odometry, odometryNoise);
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graph.add(odom12); // add both to graph
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graph.add(odom23);
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/* add measurements */
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// general noisemodel for measurements
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SharedDiagonal measurementNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.2));
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// create the measurement values - indices are (pose id, landmark id)
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Rot2 bearing11 = Rot2::fromDegrees(45),
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bearing21 = Rot2::fromDegrees(90),
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bearing32 = Rot2::fromDegrees(90);
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double range11 = sqrt(4.0+4.0),
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range21 = 2.0,
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range32 = 2.0;
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// create bearing/range factors
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BearingRangeFactor<Pose2, Point2> meas11(i1, j1, bearing11, range11, measurementNoise);
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BearingRangeFactor<Pose2, Point2> meas21(i2, j1, bearing21, range21, measurementNoise);
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BearingRangeFactor<Pose2, Point2> meas32(i3, j2, bearing32, range32, measurementNoise);
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// add the factors
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graph.add(meas11);
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graph.add(meas21);
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graph.add(meas32);
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graph.print("Full Graph");
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// initialize to noisy points
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Values initial;
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initial.insert(i1, Pose2(0.5, 0.0, 0.2));
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initial.insert(i2, Pose2(2.3, 0.1,-0.2));
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initial.insert(i3, Pose2(4.1, 0.1, 0.1));
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initial.insert(j1, Point2(1.8, 2.1));
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initial.insert(j2, Point2(4.1, 1.8));
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initial.print("initial estimate");
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// optimize using Levenberg-Marquardt optimization with an ordering from colamd
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// first using sequential elimination
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LevenbergMarquardtParams lmParams;
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lmParams.linearSolverType = LevenbergMarquardtParams::SEQUENTIAL_CHOLESKY;
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Values resultSequential = LevenbergMarquardtOptimizer(graph, initial, lmParams).optimize();
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resultSequential.print("final result (solved with a sequential solver)");
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// then using multifrontal, advanced interface
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// Note that we keep the original optimizer object so we can use the COLAMD
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// ordering it computes.
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LevenbergMarquardtOptimizer optimizer(graph, initial);
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Values resultMultifrontal = optimizer.optimize();
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resultMultifrontal.print("final result (solved with a multifrontal solver)");
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// Print marginals covariances for all variables
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Marginals marginals(graph, resultMultifrontal, Marginals::CHOLESKY);
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print(marginals.marginalCovariance(i1), "i1 covariance");
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print(marginals.marginalCovariance(i2), "i2 covariance");
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print(marginals.marginalCovariance(i3), "i3 covariance");
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print(marginals.marginalCovariance(j1), "j1 covariance");
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print(marginals.marginalCovariance(j2), "j2 covariance");
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return 0;
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}
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