Removed SLAM namespaces from SPCG example. Still needs better documentation by someone who knows what SPCG is.

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 <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 simple integer keys
+#include <gtsam/nonlinear/Key.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.
+// 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 <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>
+
+// 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>
+
+// ???
 #include <gtsam/linear/SimpleSPCGSolver.h>
 #include <gtsam/linear/SubgraphSolver.h>
 #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
-#include <gtsam/slam/pose2SLAM.h>
 
-#include <boost/shared_ptr.hpp>
+
-#include <boost/make_shared.hpp>
 
 using namespace std;
 using namespace gtsam;
-using namespace gtsam::noiseModel;
 
-/* ************************************************************************* */
+int main(int argc, char** argv) {
-int main(void) {
 
-  // 1. Create graph container and add factors to it
+  // 1. Create a factor graph container and add factors to it
-  pose2SLAM::Graph graph ;
+  NonlinearFactorGraph graph;
 
-  // 2a. Add Gaussian prior
+  // 2a. Add a prior on the first pose, setting it to the origin
-  Pose2 priorMean(0.0, 0.0, 0.0); // prior 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));
+  Pose2 prior(0.0, 0.0, 0.0); // prior at origin
-  graph.addPosePrior(1, priorMean, priorNoise);
+  noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
+  graph.add(PriorFactor<Pose2>(1, prior, priorNoise));
 
   // 2b. Add odometry factors
-  SharedDiagonal odometryNoise = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
+  // For simplicity, we will use the same noise model for each odometry factor
-  graph.addRelativePose(1, 2, Pose2(2.0, 0.0, 0.0   ), odometryNoise);
+  noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
-  graph.addRelativePose(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
+  // Create odometry (Between) factors between consecutive poses
-  graph.addRelativePose(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
+  graph.add(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, M_PI_2),    odometryNoise));
-  graph.addRelativePose(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
+  graph.add(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
+  graph.add(BetweenFactor<Pose2>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
+  graph.add(BetweenFactor<Pose2>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
 
-  // 2c. Add pose constraint
+  // 2c. Add the loop closure constraint
-  SharedDiagonal constraintUncertainty = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
+  // This factor encodes the fact that we have returned to the same pose. In real systems,
-  graph.addRelativePose(5, 2, Pose2(2.0, 0.0, M_PI_2), constraintUncertainty);
+  // 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<Pose2>(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
+  // 3. Create the data structure to hold the initialEstimate estimate to the solution
-  pose2SLAM::Values initialEstimate;
+  // For illustrative purposes, these have been deliberately set to incorrect values
-  Pose2 x1(0.5, 0.0, 0.2   ); initialEstimate.insertPose(1, x1);
+  Values initialEstimate;
-  Pose2 x2(2.3, 0.1,-0.2   ); initialEstimate.insertPose(2, x2);
+  initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2));
-  Pose2 x3(4.1, 0.1, M_PI_2); initialEstimate.insertPose(3, x3);
+  initialEstimate.insert(2, Pose2(2.3, 0.1, 1.1));
-  Pose2 x4(4.0, 2.0, M_PI  ); initialEstimate.insertPose(4, x4);
+  initialEstimate.insert(3, Pose2(2.1, 1.9, 2.8));
-  Pose2 x5(2.1, 2.1,-M_PI_2); initialEstimate.insertPose(5, x5);
+  initialEstimate.insert(4, Pose2(-.3, 2.5, 4.2));
-  initialEstimate.print("\nInitial estimate:\n  ");
+  initialEstimate.insert(5, Pose2(0.1,-0.7, 5.8));
-  cout << "initial error = " << graph.error(initialEstimate) << endl ;
+  initialEstimate.print("\nInitial Estimate:\n"); // print
 
   // 4. Single Step Optimization using Levenberg-Marquardt
-  LevenbergMarquardtParams param;
+  LevenbergMarquardtParams parameters;
-  param.verbosity = NonlinearOptimizerParams::ERROR;
+  parameters.verbosity = NonlinearOptimizerParams::ERROR;
-  param.verbosityLM = LevenbergMarquardtParams::LAMBDA;
+  parameters.verbosityLM = LevenbergMarquardtParams::LAMBDA;
-  param.linearSolverType = SuccessiveLinearizationParams::CG;
+  parameters.linearSolverType = SuccessiveLinearizationParams::CG;
 
   {
-    param.iterativeParams = boost::make_shared<SimpleSPCGSolverParameters>();
+    parameters.iterativeParams = boost::make_shared<SimpleSPCGSolverParameters>();
-    LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, param);
+    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<SubgraphSolverParameters>();
+    parameters.iterativeParams = boost::make_shared<SubgraphSolverParameters>();
-    LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, param);
+    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;
   }
 
-  {
+  return 0;
-  	Values result = graph.optimizeSPCG(initialEstimate);
-  	result.print("\nFinal result:\n");
-  }
-
-  return 0 ;
 }