Cleaned up comments and headers in examples

examples/Pose2SLAMExampleExpressions.cpp

@@ -14,20 +14,18 @@
  * @brief Expressions version of Pose2SLAMExample.cpp
  * @date Oct 2, 2014
  * @author Frank Dellaert
- * @author Yong Dian Jian
  */
 
 // The two new headers that allow using our Automatic Differentiation Expression framework
 #include <gtsam/slam/expressions.h>
 #include <gtsam/nonlinear/ExpressionFactorGraph.h>
 
-// Header order is close to far
+// For an explanation of headers below, please see Pose2SLAMExample.cpp
-#include <gtsam/nonlinear/NonlinearFactorGraph.h>
+#include <gtsam/slam/PriorFactor.h>
+#include <gtsam/slam/BetweenFactor.h>
+#include <gtsam/geometry/Pose2.h>
 #include <gtsam/nonlinear/GaussNewtonOptimizer.h>
 #include <gtsam/nonlinear/Marginals.h>
-#include <gtsam/nonlinear/Values.h>
-#include <gtsam/geometry/Pose2.h>
-#include <gtsam/inference/Key.h>
 
 using namespace std;
 using namespace gtsam;

examples/Pose2SLAMExample_graph.cpp

@@ -16,11 +16,14 @@
  * @author Frank Dellaert
  */
 
-#include <gtsam/slam/dataset.h>
+// For an explanation of headers below, please see Pose2SLAMExample.cpp
 #include <gtsam/slam/PriorFactor.h>
-#include <gtsam/nonlinear/Marginals.h>
+#include <gtsam/slam/BetweenFactor.h>
 #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
-#include <gtsam/geometry/Pose2.h>
+#include <gtsam/nonlinear/Marginals.h>
+
+// This new header allows us to read examples easily from .graph files
+#include <gtsam/slam/dataset.h>
 
 using namespace std;
 using namespace gtsam;

examples/Pose2SLAMExample_graphviz.cpp

@@ -16,11 +16,11 @@
  * @author Frank Dellaert
  */
 
+// For an explanation of headers below, please see Pose2SLAMExample.cpp
 #include <gtsam/slam/PriorFactor.h>
 #include <gtsam/slam/BetweenFactor.h>
-#include <gtsam/nonlinear/Marginals.h>
-#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
 #include <gtsam/geometry/Pose2.h>
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
 #include <fstream>
 
 using namespace std;

examples/Pose2SLAMwSPCG.cpp

@@ -16,47 +16,15 @@
  * @date June 2, 2012
  */
 
-/**
+// For an explanation of headers below, please see Pose2SLAMExample.cpp
- * 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/inference/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>
-
+#include <gtsam/geometry/Pose2.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/SubgraphSolver.h>
 #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
 
+// In contrast to that example, however, we will use a PCG solver here
+#include <gtsam/linear/SubgraphSolver.h>
+
 using namespace std;
 using namespace gtsam;
 
@@ -66,32 +34,24 @@ int main(int argc, char** argv) {
   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)
   Pose2 prior(0.0, 0.0, 0.0); // prior at origin
   noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
   graph.push_back(PriorFactor<Pose2>(1, prior, priorNoise));
 
   // 2b. Add odometry factors
-  // For simplicity, we will use the same noise model for each odometry factor
   noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
-  // Create odometry (Between) factors between consecutive poses
   graph.push_back(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, M_PI_2),    odometryNoise));
   graph.push_back(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
   graph.push_back(BetweenFactor<Pose2>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
   graph.push_back(BetweenFactor<Pose2>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
 
   // 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(Vector3(0.2, 0.2, 0.1));
   graph.push_back(BetweenFactor<Pose2>(5, 1, Pose2(0.0, 0.0, 0.0), 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, 1.1));
@@ -104,15 +64,18 @@ int main(int argc, char** argv) {
   LevenbergMarquardtParams parameters;
   parameters.verbosity = NonlinearOptimizerParams::ERROR;
   parameters.verbosityLM = LevenbergMarquardtParams::LAMBDA;
-  parameters.linearSolverType = NonlinearOptimizerParams::Iterative;
 
-  {
+  // LM is still the outer optimization loop, but by specifying "Iterative" below
+  // We indicate that an iterative linear solver should be used.
+  // In addition, the *type* of the iterativeParams decides on the type of
+  // iterative solver, in this case the SPCG (subgraph PCG)
+  parameters.linearSolverType = NonlinearOptimizerParams::Iterative;
   parameters.iterativeParams = boost::make_shared<SubgraphSolverParameters>();
+
   LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, parameters);
   Values result = optimizer.optimize();
   result.print("Final Result:\n");
   cout << "subgraph solver final error = " << graph.error(result) << endl;
-  }
 
   return 0;
 }

examples/SFMExample.cpp

@@ -15,13 +15,7 @@
  * @author  Duy-Nguyen Ta
  */
 
-/**
+// For loading the data, see the comments therein for scenario (camera rotates around cube)
- * A structure-from-motion example with landmarks
- *  - The landmarks form a 10 meter cube
- *  - The robot rotates around the landmarks, always facing towards the cube
- */
-
-// For loading the data
 #include "SFMdata.h"
 
 // Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).

examples/SFMExample_SmartFactor.cpp

@@ -17,46 +17,17 @@
  * @author  Frank Dellaert
  */
 
-/**
- * A structure-from-motion example with landmarks
- *  - The landmarks form a 10 meter cube
- *  - The robot rotates around the landmarks, always facing towards the cube
- */
-
-// For loading the data
-#include "SFMdata.h"
-
-// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
-#include <gtsam/geometry/Point2.h>
-
 // In GTSAM, measurement functions are represented as 'factors'.
-// The factor we used here is SmartProjectionPoseFactor. Every smart factor represent a single landmark,
+// The factor we used here is SmartProjectionPoseFactor.
-// The SmartProjectionPoseFactor only optimize the pose of camera, not the calibration,
+// Every smart factor represent a single landmark, seen from multiple cameras.
-// The calibration should be known.
+// The SmartProjectionPoseFactor only optimizes for the poses of a camera,
+// not the calibration, which is assumed known.
 #include <gtsam/slam/SmartProjectionPoseFactor.h>
 
-// Also, we will initialize the robot at some location using a Prior factor.
+// For an explanation of these headers, see SFMExample.cpp
-#include <gtsam/slam/PriorFactor.h>
+#include "SFMdata.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 a
-// trust-region method known as Powell's Degleg
 #include <gtsam/nonlinear/DoglegOptimizer.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 <vector>
-
 using namespace std;
 using namespace gtsam;