diff --git a/examples/Pose2SLAMExample.cpp b/examples/Pose2SLAMExample.cpp index 0777301ea..4e660a768 100644 --- a/examples/Pose2SLAMExample.cpp +++ b/examples/Pose2SLAMExample.cpp @@ -11,55 +11,126 @@ /** * @file Pose2SLAMExample.cpp - * @brief A 2D Pose SLAM example using the predefined typedefs in gtsam/slam/pose2SLAM.h + * @brief A 2D Pose SLAM example * @date Oct 21, 2010 * @author Yong Dian Jian */ -// pull in the Pose2 SLAM domain with all typedefs and helper functions defined -#include -#include +/** + * A simple 2D pose slam example + * - The robot moves in a 2 meter square + * - The robot moves 2 meters each step, turning 90 degrees after each step + * - The robot initially faces along the X axis (horizontal, to the right in 2D) + * - We have full odometry between pose + * - We have a loop closure constraint when the robot returns to the first position + */ + +// As this is a planar SLAM example, we will use Pose2 variables (x, y, theta) to represent +// the robot positions +#include +#include + +// Each variable in the system (poses) must be identified with a unique key. +// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1). +// Here we will use simple integer keys +#include + +// In GTSAM, measurement functions are represented as 'factors'. Several common factors +// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems. +// Here we will use Between factors for the relative motion described by odometry measurements. +// We will also use a Between Factor to encode the loop closure constraint +// Also, we will initialize the robot at the origin using a Prior factor. +#include +#include + +// When the factors are created, we will add them to a Factor Graph. As the factors we are using +// are nonlinear factors, we will need a Nonlinear Factor Graph. +#include + +// Finally, once all of the factors have been added to our factor graph, we will want to +// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values. +// GTSAM includes several nonlinear optimizers to perform this step. Here we will use the +// a Gauss-Newton solver +#include + +// Once the optimized values have been calculated, we can also calculate the marginal covariance +// of desired variables +#include + +// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the +// nonlinear functions around an initial linearization point, then solve the linear system +// to update the linearization point. This happens repeatedly until the solver converges +// to a consistent set of variable values. This requires us to specify an initial guess +// for each variable, held in a Values container. +#include + using namespace std; using namespace gtsam; -using namespace gtsam::noiseModel; int main(int argc, char** argv) { - // 1. Create graph container and add factors to it - pose2SLAM::Graph graph; + // 1. Create a factor graph container and add factors to it + NonlinearFactorGraph graph; - // 2a. Add Gaussian prior - Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin - SharedDiagonal priorNoise = Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1)); - graph.addPosePrior(1, priorMean, priorNoise); + // 2a. Add a prior on the first pose, setting it to the origin + // A prior factor consists of a mean and a noise model (covariance matrix) + Pose2 prior(0.0, 0.0, 0.0); // prior at origin + noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1)); + graph.add(PriorFactor(1, prior, priorNoise)); // 2b. Add odometry factors - SharedDiagonal odometryNoise = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); - graph.addRelativePose(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise); - graph.addRelativePose(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise); - graph.addRelativePose(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise); - graph.addRelativePose(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise); + // For simplicity, we will use the same noise model for each odometry factor + noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); + // Create odometry (Between) factors between consecutive poses + graph.add(BetweenFactor(1, 2, Pose2(2.0, 0.0, M_PI_2), odometryNoise)); + graph.add(BetweenFactor(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise)); + graph.add(BetweenFactor(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise)); + graph.add(BetweenFactor(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise)); - // 2c. Add pose constraint - SharedDiagonal model = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); - graph.addRelativePose(5, 2, Pose2(2.0, 0.0, M_PI_2), model); + // 2c. Add the loop closure constraint + // This factor encodes the fact that we have returned to the same pose. In real systems, + // these constraints may be identified in many ways, such as appearance-based techniques + // with camera images. + // We will use another Between Factor to enforce this constraint, with the distance set to zero, + noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); + graph.add(BetweenFactor(5, 1, Pose2(0.0, 0.0, 0.0), model)); + graph.print("\nFactor Graph:\n"); // print - // print - graph.print("\nFactor graph:\n"); // 3. Create the data structure to hold the initialEstimate estimate to the solution - pose2SLAM::Values initialEstimate; - initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2)); - initialEstimate.insertPose(2, Pose2(2.3, 0.1, -0.2)); - initialEstimate.insertPose(3, Pose2(4.1, 0.1, M_PI_2)); - initialEstimate.insertPose(4, Pose2(4.0, 2.0, M_PI)); - initialEstimate.insertPose(5, Pose2(2.1, 2.1, -M_PI_2)); - initialEstimate.print("\nInitial estimate:\n"); + // For illustrative purposes, these have been deliberately set to incorrect values + Values initialEstimate; + initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2)); + initialEstimate.insert(2, Pose2(2.3, 0.1, 1.1)); + initialEstimate.insert(3, Pose2(2.1, 1.9, 2.8)); + initialEstimate.insert(4, Pose2(-.3, 2.5, 4.2)); + initialEstimate.insert(5, Pose2(0.1,-0.7, 5.8)); + initialEstimate.print("\nInitial Estimate:\n"); // print - // 4. Single Step Optimization using Levenberg-Marquardt - pose2SLAM::Values result = graph.optimize(initialEstimate); - result.print("\nFinal result:\n"); + // 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer + // The optimizer accepts an optional set of configuration parameters, + // controlling things like convergence criteria, the type of linear + // system solver to use, and the amount of information displayed during + // optimization. We will set a few parameters as a demonstration. + GaussNewtonParams parameters; + // Stop iterating once the change in error between steps is less than this value + parameters.relativeErrorTol = 1e-5; + // Do not perform more than N iteration steps + parameters.maxIterations = 100; + // Create the optimizer ... + GaussNewtonOptimizer optimizer(graph, initialEstimate, parameters); + // ... and optimize + Values result = optimizer.optimize(); + result.print("Final Result:\n"); + + // 5. Calculate and print marginal covariances for all variables + Marginals marginals(graph, result); + cout << "Pose 1 covariance:\n" << marginals.marginalCovariance(1) << endl; + cout << "Pose 2 covariance:\n" << marginals.marginalCovariance(2) << endl; + cout << "Pose 3 covariance:\n" << marginals.marginalCovariance(3) << endl; + cout << "Pose 4 covariance:\n" << marginals.marginalCovariance(4) << endl; + cout << "Pose 5 covariance:\n" << marginals.marginalCovariance(5) << endl; return 0; } diff --git a/examples/Pose2SLAMExample_advanced.cpp b/examples/Pose2SLAMExample_advanced.cpp deleted file mode 100644 index b5fcddfcf..000000000 --- a/examples/Pose2SLAMExample_advanced.cpp +++ /dev/null @@ -1,82 +0,0 @@ -/* ---------------------------------------------------------------------------- - - * 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 Pose2SLAMExample_advanced.cpp - * @brief Simple Pose2SLAM Example using - * pre-built pose2SLAM domain - * @author Chris Beall - */ - -// pull in the Pose2 SLAM domain with all typedefs and helper functions defined -#include -#include -#include -#include -#include - -#include -#include -#include - -using namespace std; -using namespace gtsam; -using namespace gtsam::noiseModel; - -int main(int argc, char** argv) { - /* 1. create graph container and add factors to it */ - pose2SLAM::Graph graph; - - /* 2.a add prior */ - Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin - SharedDiagonal priorNoise = Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta - graph.addPosePrior(1, priorMean, priorNoise); // add directly to graph - - /* 2.b add odometry */ - SharedDiagonal odometryNoise = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta - Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case) - graph.addRelativePose(1, 2, odometry, odometryNoise); - graph.addRelativePose(2, 3, odometry, odometryNoise); - graph.print("full graph"); - - /* 3. Create the data structure to hold the initial estimate to the solution - * initialize to noisy points */ - pose2SLAM::Values initial; - initial.insertPose(1, Pose2(0.5, 0.0, 0.2)); - initial.insertPose(2, Pose2(2.3, 0.1, -0.2)); - initial.insertPose(3, Pose2(4.1, 0.1, 0.1)); - initial.print("initial estimate"); - - /* 4.2.1 Alternatively, you can go through the process step by step - * Choose an ordering */ - Ordering ordering = *graph.orderingCOLAMD(initial); - - /* 4.2.2 set up solver and optimize */ - LevenbergMarquardtParams params; - params.absoluteErrorTol = 1e-15; - params.relativeErrorTol = 1e-15; - params.ordering = ordering; - LevenbergMarquardtOptimizer optimizer(graph, initial, params); - - pose2SLAM::Values result = optimizer.optimize(); - result.print("final result"); - - /* Get covariances */ - Marginals marginals(graph, result, Marginals::CHOLESKY); - Matrix covariance1 = marginals.marginalCovariance(1); - Matrix covariance2 = marginals.marginalCovariance(2); - - print(covariance1, "Covariance1"); - print(covariance2, "Covariance2"); - - return 0; -} -