119 lines
5.3 KiB
C++
119 lines
5.3 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 Pose2SLAMwSPCG.cpp
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* @brief A 2D Pose SLAM example using the SimpleSPCGSolver.
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* @author Yong-Dian Jian
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* @date June 2, 2012
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*/
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/**
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* A simple 2D pose slam example solved using a Conjugate-Gradient method
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* - The robot moves in a 2 meter square
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* - The robot moves 2 meters each step, turning 90 degrees after each step
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* - The robot initially faces along the X axis (horizontal, to the right in 2D)
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* - We have full odometry between pose
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* - We have a loop closure constraint when the robot returns to the first position
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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
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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) 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 simple integer keys
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#include <gtsam/nonlinear/Key.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 Between factors for the relative motion described by odometry measurements.
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// We will also use a Between Factor to encode the loop closure constraint
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// Also, we will initialize the robot at the origin using a Prior factor.
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.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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// 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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#include <gtsam/linear/SubgraphSolver.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.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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// 1. Create a factor graph container and add factors to it
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NonlinearFactorGraph graph;
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// 2a. Add a prior on the first pose, setting it to the origin
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// 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 at origin
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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graph.add(PriorFactor<Pose2>(1, prior, priorNoise));
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// 2b. Add odometry factors
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// For simplicity, we will use the same noise model for each odometry factor
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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// Create odometry (Between) factors between consecutive poses
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graph.add(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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graph.add(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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graph.add(BetweenFactor<Pose2>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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graph.add(BetweenFactor<Pose2>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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// 2c. Add the loop closure constraint
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// This factor encodes the fact that we have returned to the same pose. In real systems,
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// these constraints may be identified in many ways, such as appearance-based techniques
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// with camera images.
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// We will use another Between Factor to enforce this constraint, with the distance set to zero,
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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graph.add(BetweenFactor<Pose2>(5, 1, Pose2(0.0, 0.0, 0.0), model));
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graph.print("\nFactor Graph:\n"); // print
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// 3. Create the data structure to hold the initialEstimate estimate to the solution
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// For illustrative purposes, these have been deliberately set to incorrect values
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Values initialEstimate;
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initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insert(2, Pose2(2.3, 0.1, 1.1));
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initialEstimate.insert(3, Pose2(2.1, 1.9, 2.8));
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initialEstimate.insert(4, Pose2(-.3, 2.5, 4.2));
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initialEstimate.insert(5, Pose2(0.1,-0.7, 5.8));
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initialEstimate.print("\nInitial Estimate:\n"); // print
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// 4. Single Step Optimization using Levenberg-Marquardt
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LevenbergMarquardtParams parameters;
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parameters.verbosity = NonlinearOptimizerParams::ERROR;
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parameters.verbosityLM = LevenbergMarquardtParams::LAMBDA;
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parameters.linearSolverType = SuccessiveLinearizationParams::CONJUGATE_GRADIENT;
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{
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parameters.iterativeParams = boost::make_shared<SubgraphSolverParameters>();
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LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, parameters);
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Values result = optimizer.optimize();
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result.print("Final Result:\n");
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cout << "subgraph solver final error = " << graph.error(result) << endl;
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}
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return 0;
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}
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