diff --git a/examples/Pose2SLAMwSPCG.cpp b/examples/Pose2SLAMwSPCG.cpp index e2f3801f7..38a2e18e4 100644 --- a/examples/Pose2SLAMwSPCG.cpp +++ b/examples/Pose2SLAMwSPCG.cpp @@ -16,79 +16,115 @@ * @date June 2, 2012 */ +/** + * A simple 2D pose slam example solved using a Conjugate-Gradient method + * - 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 + +// 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 + +// ??? #include #include #include -#include -#include -#include + using namespace std; using namespace gtsam; -using namespace gtsam::noiseModel; -/* ************************************************************************* */ -int main(void) { +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 constraintUncertainty = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); - graph.addRelativePose(5, 2, Pose2(2.0, 0.0, M_PI_2), constraintUncertainty); + // 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 estinmate to the solution - pose2SLAM::Values initialEstimate; - Pose2 x1(0.5, 0.0, 0.2 ); initialEstimate.insertPose(1, x1); - Pose2 x2(2.3, 0.1,-0.2 ); initialEstimate.insertPose(2, x2); - Pose2 x3(4.1, 0.1, M_PI_2); initialEstimate.insertPose(3, x3); - Pose2 x4(4.0, 2.0, M_PI ); initialEstimate.insertPose(4, x4); - Pose2 x5(2.1, 2.1,-M_PI_2); initialEstimate.insertPose(5, x5); - initialEstimate.print("\nInitial estimate:\n "); - cout << "initial error = " << graph.error(initialEstimate) << endl ; + // 3. Create the data structure to hold the initialEstimate estimate to the solution + // 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 - LevenbergMarquardtParams param; - param.verbosity = NonlinearOptimizerParams::ERROR; - param.verbosityLM = LevenbergMarquardtParams::LAMBDA; - param.linearSolverType = SuccessiveLinearizationParams::CG; + LevenbergMarquardtParams parameters; + parameters.verbosity = NonlinearOptimizerParams::ERROR; + parameters.verbosityLM = LevenbergMarquardtParams::LAMBDA; + parameters.linearSolverType = SuccessiveLinearizationParams::CG; { - param.iterativeParams = boost::make_shared(); - LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, param); + parameters.iterativeParams = boost::make_shared(); + LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, parameters); Values result = optimizer.optimize(); - result.print("\nFinal result:\n"); + result.print("Final Result:\n"); cout << "simple spcg solver final error = " << graph.error(result) << endl; } { - param.iterativeParams = boost::make_shared(); - LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, param); + parameters.iterativeParams = boost::make_shared(); + LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, parameters); Values result = optimizer.optimize(); - result.print("\nFinal result:\n"); + result.print("Final Result:\n"); cout << "subgraph solver final error = " << graph.error(result) << endl; } - { - Values result = graph.optimizeSPCG(initialEstimate); - result.print("\nFinal result:\n"); - } - - return 0 ; + return 0; }