Metis ordering example

examples/METISOrderingExample.cpp

@@ -0,0 +1,100 @@
+/* ----------------------------------------------------------------------------
+
+ * 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 METISOrdering.cpp
+ * @brief Simple robot motion example, with prior and two odometry measurements, using a METIS ordering
+ * @author Frank Dellaert
+ * @author Andrew Melim
+ */
+
+/**
+ * Example of a simple 2D localization example
+ *  - Robot poses are facing along the X axis (horizontal, to the right in 2D)
+ *  - The robot moves 2 meters each step
+ *  - We have full odometry between poses
+ */
+
+// We will use Pose2 variables (x, y, theta) to represent the robot positions
+#include <gtsam/geometry/Pose2.h>
+
+// 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.
+// Also, we will initialize the robot at the origin using a Prior factor.
+#include <gtsam/slam/PriorFactor.h>
+#include <gtsam/slam/BetweenFactor.h>
+
+// 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 <gtsam/nonlinear/NonlinearFactorGraph.h>
+
+// 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
+// Levenberg-Marquardt solver
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+
+// Once the optimized values have been calculated, we can also calculate the marginal covariance
+// of desired variables
+#include <gtsam/nonlinear/Marginals.h>
+
+// 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 <gtsam/nonlinear/Values.h>
+
+using namespace std;
+using namespace gtsam;
+
+int main(int argc, char** argv) {
+
+  // Create an empty nonlinear factor graph
+  NonlinearFactorGraph graph;
+
+  // 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 priorMean(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<Pose2>(1, priorMean, priorNoise));
+
+  // Add odometry factors
+  Pose2 odometry(2.0, 0.0, 0.0);
+  // 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<Pose2>(1, 2, odometry, odometryNoise));
+  graph.add(BetweenFactor<Pose2>(2, 3, odometry, odometryNoise));
+  graph.print("\nFactor Graph:\n"); // print
+
+  // Create the data structure to hold the initialEstimate estimate to the solution
+  // For illustrative purposes, these have been deliberately set to incorrect values
+  Values initial;
+  initial.insert(1, Pose2(0.5, 0.0, 0.2));
+  initial.insert(2, Pose2(2.3, 0.1, -0.2));
+  initial.insert(3, Pose2(4.1, 0.1, 0.1));
+  initial.print("\nInitial Estimate:\n"); // print
+
+  // optimize using Levenberg-Marquardt optimization
+  Values result = LevenbergMarquardtOptimizer(graph, initial).optimize();
+  result.print("Final Result:\n");
+
+  // Calculate and print marginal covariances for all variables
+  cout.precision(2);
+  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;
+
+  return 0;
+}

gtsam/3rdparty/metis-5.1.0/libmetis/CMakeLists.txt

@@ -8,8 +8,6 @@ if(UNIX)
   target_link_libraries(metis m)
 endif()
 
-
-
 if(WIN32)
 		set_target_properties(metis PROPERTIES
 			PREFIX ""
@@ -19,4 +17,3 @@ endif()
 install(TARGETS metis EXPORT GTSAM-exports LIBRARY DESTINATION lib ARCHIVE DESTINATION lib RUNTIME DESTINATION bin)
 list(APPEND GTSAM_EXPORTED_TARGETS metis)
 set(GTSAM_EXPORTED_TARGETS "${GTSAM_EXPORTED_TARGETS}" PARENT_SCOPE)
-