148 lines
6.0 KiB
C++
148 lines
6.0 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 PlanarSLAMSelfContained.cpp
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* @brief Simple robotics example from tutorial Figure 1.1 (left), with all typedefs
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* internal to this script.
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* @author Alex Cunningham
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*/
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#include <cmath>
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#include <iostream>
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// for all nonlinear keys
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#include <gtsam/nonlinear/Key.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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// for modeling measurement uncertainty - all models included here
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#include <gtsam/linear/NoiseModel.h>
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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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// implementations for structures - needed if self-contained, and these should be included last
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#include <gtsam/nonlinear/TupleValues-inl.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph-inl.h>
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#include <gtsam/nonlinear/NonlinearOptimizer-inl.h>
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#include <gtsam/linear/GaussianSequentialSolver.h>
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#include <gtsam/linear/GaussianMultifrontalSolver.h>
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// Main typedefs
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typedef gtsam::TypedSymbol<gtsam::Pose2, 'x'> PoseKey; // Key for poses, with type included
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typedef gtsam::TypedSymbol<gtsam::Point2,'l'> PointKey; // Key for points, with type included
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typedef gtsam::LieValues<PoseKey> PoseValues; // config type for poses
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typedef gtsam::LieValues<PointKey> PointValues; // config type for points
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typedef gtsam::TupleValues2<PoseValues, PointValues> Values; // main config with two variable classes
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typedef gtsam::NonlinearFactorGraph<Values> Graph; // graph structure
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typedef gtsam::NonlinearOptimizer<Graph,Values,gtsam::GaussianFactorGraph,gtsam::GaussianSequentialSolver> OptimizerSeqential; // optimization engine for this domain
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typedef gtsam::NonlinearOptimizer<Graph,Values,gtsam::GaussianFactorGraph,gtsam::GaussianMultifrontalSolver> OptimizerMultifrontal; // optimization engine for this domain
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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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PoseKey x1(1), x2(2), x3(3);
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PointKey l1(1), l2(2);
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// create graph container and add factors to it
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Graph::shared_ptr graph(new Graph);
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/* add prior */
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// gaussian for prior
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SharedDiagonal prior_model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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Pose2 prior_measurement(0.0, 0.0, 0.0); // prior at origin
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PriorFactor<Values, PoseKey> posePrior(x1, prior_measurement, prior_model); // 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 odom_model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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Pose2 odom_measurement(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<Values,PoseKey> odom12(x1, x2, odom_measurement, odom_model);
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BetweenFactor<Values,PoseKey> odom23(x2, x3, odom_measurement, odom_model);
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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 meas_model = 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+4),
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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<Values, PoseKey, PointKey> meas11(x1, l1, bearing11, range11, meas_model);
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BearingRangeFactor<Values, PoseKey, PointKey> meas21(x2, l1, bearing21, range21, meas_model);
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BearingRangeFactor<Values, PoseKey, PointKey> meas32(x3, l2, bearing32, range32, meas_model);
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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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boost::shared_ptr<Values> initial(new Values);
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initial->insert(x1, Pose2(0.5, 0.0, 0.2));
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initial->insert(x2, Pose2(2.3, 0.1,-0.2));
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initial->insert(x3, Pose2(4.1, 0.1, 0.1));
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initial->insert(l1, Point2(1.8, 2.1));
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initial->insert(l2, 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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OptimizerSeqential::shared_values resultSequential = OptimizerSeqential::optimizeLM(*graph, *initial);
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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 how we create an optimizer, call LM, then we get access to covariances
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Ordering::shared_ptr ordering = graph->orderingCOLAMD(*initial);
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OptimizerMultifrontal optimizerMF(graph, initial, ordering);
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OptimizerMultifrontal resultMF = optimizerMF.levenbergMarquardt();
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resultMF.values()->print("final result (solved with a multifrontal solver)");
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// Print marginals covariances for all variables
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print(resultMF.marginalCovariance(x1).second, "x1 covariance");
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print(resultMF.marginalCovariance(x2).second, "x2 covariance");
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print(resultMF.marginalCovariance(x3).second, "x3 covariance");
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print(resultMF.marginalCovariance(l1).second, "l1 covariance");
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print(resultMF.marginalCovariance(l2).second, "l2 covariance");
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return 0;
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}
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