Removed SLAM namespaces from Localization Example

examples/LocalizationExample.cpp

@@ -15,87 +15,166 @@
  * @author Frank Dellaert
  */
 
-// pull in the 2D PoseSLAM domain with all typedefs and helper functions defined
+/**
-#include <gtsam/slam/pose2SLAM.h>
+ * A simple 2D pose slam example with "GPS" measurements
+ *  - The robot moves forward 2 meter each iteration
+ *  - The robot initially faces along the X axis (horizontal, to the right in 2D)
+ *  - We have full odometry between pose
+ *  - We have "GPS-like" measurements implemented with a custom factor
+ */
 
-// include this for marginals
+// 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>
+
+// 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.
+// Because we have global measurements in the form of "GPS-like" measurements, we don't
+// actually need to provide an initial position prior in this example. We will create our
+// custom factor shortly.
+#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>
+
+// 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
+// standard 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>
 
-#include <iomanip>
-#include <iostream>
 
 using namespace std;
 using namespace gtsam;
-using namespace gtsam::noiseModel;
 
-/**
+
- * UnaryFactor
+// Before we begin the example, we must create a custom unary factor to implement a
- * Example on how to create a GPS-like factor on position alone.
+// "GPS-like" functionality. Because standard GPS measurements provide information
- */
+// only on the position, and not on the orientation, we cannot use a simple prior to
+// properly model this measurement.
+//
+// The factor will be a unary factor, affect only a single system variable. It will
+// also use a standard Gaussian noise model. Hence, we will derive our new factor from
+// the NoiseModelFactor1.
+#include <gtsam/nonlinear/NonlinearFactor.h>
 class UnaryFactor: public NoiseModelFactor1<Pose2> {
-  double mx_, my_; ///< X and Y measurements
+
+  // The factor will hold a measurement consisting of an (X,Y) location
+  Point2 measurement_;
 
 public:
+  /// shorthand for a smart pointer to a factor
+  typedef boost::shared_ptr<UnaryFactor> shared_ptr;
+
+  // The constructor requires the variable key, the (X, Y) measurement value, and the noise model
   UnaryFactor(Key j, double x, double y, const SharedNoiseModel& model):
-    NoiseModelFactor1<Pose2>(model, j), mx_(x), my_(y) {}
+    NoiseModelFactor1<Pose2>(model, j), measurement_(x, y) {}
 
   virtual ~UnaryFactor() {}
 
-  Vector evaluateError(const Pose2& q,
+  // By using the NoiseModelFactor base classes, the only two function that must be overridden.
-                       boost::optional<Matrix&> H = boost::none) const
+  // The first is the 'evaluateError' function. This function implements the desired measurement
+  // function, returning a vector of errors when evaluated at the provided variable value. It
+  // must also calculate the Jacobians for this measurement function, if requested.
+  Vector evaluateError(const Pose2& pose, boost::optional<Matrix&> H = boost::none) const
   {
-    if (H) (*H) = Matrix_(2,3, 1.0,0.0,0.0, 0.0,1.0,0.0);
+    // The measurement function for a GPS-like measurement is simple:
-    return Vector_(2, q.x() - mx_, q.y() - my_);
+    // error_x = pose.x - measurement.x
+    // error_y = pose.y - measurement.y
+    // Consequently, the Jacobians are:
+    // [ derror_x/dx  derror_x/dy  derror_x/dtheta ] = [1 0 0]
+    // [ derror_y/dx  derror_y/dy  derror_y/dtheta ] = [0 1 0]
+    if (H)
+      (*H) = Matrix_(2,3, 1.0,0.0,0.0, 0.0,1.0,0.0);
+
+    return Vector_(2, pose.x() - measurement_.x(), pose.y() - measurement_.y());
   }
+
+  // The second is a 'clone' function that allows the factor to be copied. Under most
+  // circumstances, the following code that employs the default copy constructor should
+  // work fine.
+  virtual gtsam::NonlinearFactor::shared_ptr clone() const {
+    return boost::static_pointer_cast<gtsam::NonlinearFactor>(
+        gtsam::NonlinearFactor::shared_ptr(new UnaryFactor(*this))); }
+
+  // Additionally, custom factors should really provide specific implementations of
+  // 'equals' to ensure proper operation will all GTSAM functionality, and a custom
+  // 'print' function, if desired.
+  virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
+    const UnaryFactor* e = dynamic_cast<const UnaryFactor*> (&expected);
+    return e != NULL && NoiseModelFactor1::equals(*e, tol) && this->measurement_.equals(e->measurement_, tol);
+  }
+
+  virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
+    std::cout << s << "UnaryFactor(" << keyFormatter(this->key()) << ")\n";
+    measurement_.print("  measurement: ");
+    this->noiseModel_->print("  noise model: ");
+  }
+
 };
 
-/**
+
- * Example of a more complex 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 have unary measurement factors at eacht time step
- */
 int main(int argc, char** argv) {
 
-  // create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
+  // 1. Create a factor graph container and add factors to it
-	pose2SLAM::Graph graph;
+  NonlinearFactorGraph graph;
 
-	// add two odometry factors
+  // 2a. 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>(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise));
+  graph.add(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, 0.0), odometryNoise));
 
-	// add unary measurement factors, like GPS, on all three poses
+  // 2b. Add "GPS-like" measurements
-	SharedDiagonal noiseModel = Diagonal::Sigmas(Vector_(2, 0.1, 0.1)); // 10cm std on x,y
+  // We will use our custom UnaryFactor for this.
-	graph.push_back(boost::make_shared<UnaryFactor>(1, 0, 0, noiseModel));
+  noiseModel::Diagonal::shared_ptr unaryNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1)); // 10cm std on x,y
-	graph.push_back(boost::make_shared<UnaryFactor>(2, 2, 0, noiseModel));
+  graph.add(UnaryFactor(1, 0.0, 0.0, unaryNoise));
-	graph.push_back(boost::make_shared<UnaryFactor>(3, 4, 0, noiseModel));
+  graph.add(UnaryFactor(3, 4.0, 0.0, unaryNoise));
+  graph.print("\nFactor Graph:\n"); // print
 
-	// print
+  // 3. 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(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, 0.1));
+  initialEstimate.print("\nInitial Estimate:\n"); // print
 
-	// create (deliberatly inaccurate) initial estimate
+  // 4. Optimize using Levenberg-Marquardt optimization. The optimizer
-	pose2SLAM::Values initialEstimate;
+  // accepts an optional set of configuration parameters, controlling
-	initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
+  // things like convergence criteria, the type of linear system solver
-	initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2));
+  // to use, and the amount of information displayed during optimization.
-	initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1));
+  // Here we will use the default set of parameters.  See the
-	initialEstimate.print("\nInitial estimate:\n  ");
+  // documentation for the full set of parameters.
+  LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
+  Values result = optimizer.optimize();
+  result.print("Final Result:\n");
 
-	// use an explicit Optimizer object
+  // 5. Calculate and print marginal covariances for all variables
-	LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
+  Marginals marginals(graph, result);
-	pose2SLAM::Values result = optimizer.optimize();
+  cout << "Pose 1 covariance:\n" << marginals.marginalCovariance(1) << endl;
-	result.print("\nFinal result:\n  ");
+  cout << "Pose 2 covariance:\n" << marginals.marginalCovariance(2) << endl;
+  cout << "Pose 3 covariance:\n" << marginals.marginalCovariance(3) << endl;
 
-	// Query the marginals
+  return 0;
-	Marginals marginals(graph, result);
-	cout.precision(2);
-  cout << "\nP1:\n" << marginals.marginalCovariance(1) << endl;
-  cout << "\nP2:\n" << marginals.marginalCovariance(2) << endl;
-  cout << "\nP3:\n" << marginals.marginalCovariance(3) << endl;
-
-	return 0;
 }
-