142 lines
6.4 KiB
C++
142 lines
6.4 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.cpp
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* @brief Simple robotics example using odometry measurements and bearing-range (laser) measurements
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* @author Alex Cunningham
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*/
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/**
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* A simple 2D planar slam example with landmarks
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* - The robot and landmarks are on a 2 meter grid
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* - Robot poses are facing along the X axis (horizontal, to the right in 2D)
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* - The robot moves 2 meters each step
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* - We have full odometry between poses
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* - We have bearing and range information for measurements
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* - Landmarks are 2 meters away from the robot trajectory
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*/
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// As this is a planar SLAM example, we will use Pose2 variables (x, y, theta) to represent
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// the robot positions and Point2 variables (x, y) to represent the landmark coordinates.
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/geometry/Point2.h>
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// Each variable in the system (poses and landmarks) must be identified with a unique key.
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// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
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// Here we will use Symbols
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#include <gtsam/inference/Symbol.h>
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// In GTSAM, measurement functions are represented as 'factors'. Several common factors
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// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
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// Here we will use a RangeBearing factor for the range-bearing measurements to identified
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// landmarks, and Between factors for the relative motion described by odometry measurements.
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// Also, we will initialize the robot at the origin using a Prior factor.
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/sam/BearingRangeFactor.h>
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// When the factors are created, we will add them to a Factor Graph. As the factors we are using
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// are nonlinear factors, we will need a Nonlinear Factor Graph.
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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// Finally, once all of the factors have been added to our factor graph, we will want to
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// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values.
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// GTSAM includes several nonlinear optimizers to perform this step. Here we will use the
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// common Levenberg-Marquardt solver
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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// Once the optimized values have been calculated, we can also calculate the marginal covariance
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// of desired variables
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#include <gtsam/nonlinear/Marginals.h>
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// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
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// nonlinear functions around an initial linearization point, then solve the linear system
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// to update the linearization point. This happens repeatedly until the solver converges
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// to a consistent set of variable values. This requires us to specify an initial guess
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// for each variable, held in a Values container.
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#include <gtsam/nonlinear/Values.h>
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using namespace std;
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using namespace gtsam;
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int main(int argc, char** argv) {
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// Create a factor graph
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NonlinearFactorGraph graph;
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// Create the keys we need for this simple example
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static Symbol x1('x',1), x2('x',2), x3('x',3);
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static Symbol l1('l',1), l2('l',2);
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// Add a prior on pose x1 at the origin. A prior factor consists of a mean and a noise model (covariance matrix)
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Pose2 prior(0.0, 0.0, 0.0); // prior mean is at origin
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
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graph.addPrior(x1, prior, priorNoise); // add directly to graph
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// Add two odometry factors
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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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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
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graph.emplace_shared<BetweenFactor<Pose2> >(x1, x2, odometry, odometryNoise);
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graph.emplace_shared<BetweenFactor<Pose2> >(x2, x3, odometry, odometryNoise);
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// Add Range-Bearing measurements to two different landmarks
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// create a noise model for the landmark measurements
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noiseModel::Diagonal::shared_ptr measurementNoise = noiseModel::Diagonal::Sigmas(Vector2(0.1, 0.2)); // 0.1 rad std on bearing, 20cm on range
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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 = std::sqrt(4.0+4.0),
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range21 = 2.0,
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range32 = 2.0;
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// Add Bearing-Range factors
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graph.emplace_shared<BearingRangeFactor<Pose2, Point2> >(x1, l1, bearing11, range11, measurementNoise);
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graph.emplace_shared<BearingRangeFactor<Pose2, Point2> >(x2, l1, bearing21, range21, measurementNoise);
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graph.emplace_shared<BearingRangeFactor<Pose2, Point2> >(x3, l2, bearing32, range32, measurementNoise);
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// Print
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graph.print("Factor Graph:\n");
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// Create (deliberately inaccurate) initial estimate
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Values initialEstimate;
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initialEstimate.insert(x1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insert(x2, Pose2(2.3, 0.1,-0.2));
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initialEstimate.insert(x3, Pose2(4.1, 0.1, 0.1));
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initialEstimate.insert(l1, Point2(1.8, 2.1));
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initialEstimate.insert(l2, Point2(4.1, 1.8));
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// Print
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initialEstimate.print("Initial Estimate:\n");
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// Optimize using Levenberg-Marquardt optimization. The optimizer
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// accepts an optional set of configuration parameters, controlling
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// things like convergence criteria, the type of linear system solver
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// to use, and the amount of information displayed during optimization.
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// Here we will use the default set of parameters. See the
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// documentation for the full set of parameters.
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LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
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Values result = optimizer.optimize();
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result.print("Final Result:\n");
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// Calculate and print marginal covariances for all variables
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Marginals marginals(graph, result);
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print(marginals.marginalCovariance(x1), "x1 covariance");
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print(marginals.marginalCovariance(x2), "x2 covariance");
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print(marginals.marginalCovariance(x3), "x3 covariance");
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print(marginals.marginalCovariance(l1), "l1 covariance");
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print(marginals.marginalCovariance(l2), "l2 covariance");
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return 0;
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}
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