Removed SLAM namespaces from OdometryExample

examples/OdometryExample.cpp

@@ -15,62 +15,91 @@
  * @author Frank Dellaert
  */
 
-// pull in the 2D PoseSLAM domain with all typedefs and helper functions defined
-#include <gtsam/slam/pose2SLAM.h>
-
-// include this for marginals
-#include <gtsam/nonlinear/Marginals.h>
-
-#include <iomanip>
-#include <iostream>
-
-using namespace std;
-using namespace gtsam;
-using namespace gtsam::noiseModel;
-
 /**
  * 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
  */
+
+// As this is a planar SLAM example, we will use Pose2 variables (x, y, theta) to represent
+// the robot positions
+#include <gtsam/geometry/Pose2.h>
+#include <gtsam/geometry/Point2.h>
+
+// 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 symbols
+#include <gtsam/nonlinear/Symbol.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 the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
+  // Create a factor graph container
-	pose2SLAM::Graph graph;
+  NonlinearFactorGraph graph;
 
-	// add a Gaussian prior on pose x_1
+  // Add a prior on the first pose, setting it to the origin
-	Pose2 priorMean(0.0, 0.0, 0.0); // prior mean is at origin
+  // A prior factor consists of a mean and a noise model (covariance matrix)
-	SharedDiagonal priorNoise = Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
+  Pose2 prior(0.0, 0.0, 0.0); // prior at origin
-	graph.addPosePrior(1, priorMean, priorNoise); // add directly to graph
+  noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
+  graph.add(PriorFactor<Pose2>(Symbol('x', 1), prior, priorNoise));
 
-	// add two odometry factors
+  // Add odometry factors
-	Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
+  // For simplicity, we will use the same noise model for each odometry factor
-	SharedDiagonal odometryNoise = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
+  noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
-	graph.addRelativePose(1, 2, odometry, odometryNoise);
+  // Create odometry (Between) factors between consecutive poses
-	graph.addRelativePose(2, 3, odometry, odometryNoise);
+  graph.add(BetweenFactor<Pose2>(Symbol('x', 1), Symbol('x', 2), Pose2(2.0, 0.0, 0.0), odometryNoise));
+  graph.add(BetweenFactor<Pose2>(Symbol('x', 2), Symbol('x', 3), Pose2(2.0, 0.0, 0.0), odometryNoise));
+  graph.print("\nFactor Graph:\n"); // print
 
-	// print
+  // Create the data structure to hold the initialEstimate estimate to the solution
-	graph.print("\nFactor graph:\n");
+  // For illustrative purposes, these have been deliberately set to incorrect values
+  Values initialEstimate;
+  initialEstimate.insert(Symbol('x', 1), Pose2(0.5, 0.0, 0.2));
+  initialEstimate.insert(Symbol('x', 2), Pose2(2.3, 0.1, -0.2));
+  initialEstimate.insert(Symbol('x', 3), Pose2(4.1, 0.1, 0.1));
+  initialEstimate.print("\nInitial Estimate:\n"); // print
 
-	// create (deliberatly inaccurate) initial estimate
+  // optimize using Levenberg-Marquardt optimization
-	pose2SLAM::Values initialEstimate;
+  Values result = LevenbergMarquardtOptimizer(graph, initialEstimate).optimize();
-	initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
+  result.print("Final Result:\n");
-	initialEstimate.insertPose(2, Pose2(2.3, 0.1, -0.2));
-	initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1));
-	initialEstimate.print("\nInitial estimate:\n  ");
 
-	// optimize using Levenberg-Marquardt optimization with an ordering from colamd
+  // Calculate and print marginal covariances for all variables
-	pose2SLAM::Values result = graph.optimize(initialEstimate);
+  Marginals marginals(graph, result);
-	result.print("\nFinal result:\n  ");
+  cout << "Pose 1 covariance:\n" << marginals.marginalCovariance(Symbol('x', 1)) << endl;
+  cout << "Pose 2 covariance:\n" << marginals.marginalCovariance(Symbol('x', 2)) << endl;
+  cout << "Pose 3 covariance:\n" << marginals.marginalCovariance(Symbol('x', 3)) << endl;
 
-	// Query the marginals
+  return 0;
-	cout.precision(2);
-	Marginals marginals = graph.marginals(result);
-	cout << "\nP1:\n" << marginals.marginalCovariance(1) << endl;
-	cout << "\nP2:\n" << marginals.marginalCovariance(2) << endl;
-	cout << "\nP3:\n" << marginals.marginalCovariance(3) << endl;
-
-	return 0;
 }
-