diff --git a/cmake/example_cmake_find_gtsam/CMakeLists.txt b/cmake/example_cmake_find_gtsam/CMakeLists.txt new file mode 100644 index 000000000..9a4be4d70 --- /dev/null +++ b/cmake/example_cmake_find_gtsam/CMakeLists.txt @@ -0,0 +1,17 @@ +# This file shows how to build and link a user project against GTSAM using CMake +################################################################################### +# To create your own project, replace "example" with the actual name of your project +cmake_minimum_required(VERSION 3.0) +project(example CXX) + +# Find GTSAM, either from a local build, or from a Debian/Ubuntu package. +find_package(GTSAM REQUIRED) + +add_executable(example + main.cpp +) + +# By using CMake exported targets, a simple "link" dependency introduces the +# include directories (-I) flags, links against Boost, and add any other +# required build flags (e.g. C++11, etc.) +target_link_libraries(example PRIVATE gtsam) diff --git a/cmake/example_cmake_find_gtsam/main.cpp b/cmake/example_cmake_find_gtsam/main.cpp new file mode 100644 index 000000000..4d93e1b19 --- /dev/null +++ b/cmake/example_cmake_find_gtsam/main.cpp @@ -0,0 +1,127 @@ +/* ---------------------------------------------------------------------------- + * 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.cpp + * @brief A 2D Pose SLAM example + * @date Oct 21, 2010 + * @author Yong Dian Jian + */ + +/** + * 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 + */ + +// In planar SLAM example we use Pose2 variables (x, y, theta) to represent the robot poses +#include + +// We will use simple integer Keys to refer to the robot poses. +#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; + +int main(int argc, char** argv) { + + // 1. Create a factor graph container and add factors to it + NonlinearFactorGraph graph; + + // 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) + noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1)); + graph.emplace_shared >(1, Pose2(0, 0, 0), priorNoise); + + // For simplicity, we will use the same noise model for odometry and loop closures + noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1)); + + // 2b. Add odometry factors + // Create odometry (Between) factors between consecutive poses + graph.emplace_shared >(1, 2, Pose2(2, 0, 0 ), model); + graph.emplace_shared >(2, 3, Pose2(2, 0, M_PI_2), model); + graph.emplace_shared >(3, 4, Pose2(2, 0, M_PI_2), model); + graph.emplace_shared >(4, 5, Pose2(2, 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: + graph.emplace_shared >(5, 2, Pose2(2, 0, M_PI_2), model); + graph.print("\nFactor Graph:\n"); // print + + // 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, -0.2 )); + initialEstimate.insert(3, Pose2(4.1, 0.1, M_PI_2)); + initialEstimate.insert(4, Pose2(4.0, 2.0, M_PI )); + initialEstimate.insert(5, Pose2(2.1, 2.1, -M_PI_2)); + initialEstimate.print("\nInitial Estimate:\n"); // print + + // 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 + cout.precision(3); + Marginals marginals(graph, result); + cout << "x1 covariance:\n" << marginals.marginalCovariance(1) << endl; + cout << "x2 covariance:\n" << marginals.marginalCovariance(2) << endl; + cout << "x3 covariance:\n" << marginals.marginalCovariance(3) << endl; + cout << "x4 covariance:\n" << marginals.marginalCovariance(4) << endl; + cout << "x5 covariance:\n" << marginals.marginalCovariance(5) << endl; + + return 0; +}