Cleaned up example

examples/VisualISAM2Example.cpp

@@ -11,8 +11,8 @@
 
 /**
  * @file    VisualISAM2Example.cpp
- * @brief   A visualSLAM example for the structure-from-motion problem on a simulated dataset
+ * @brief   A visualSLAM example for the structure-from-motion problem on a
- * This version uses iSAM2 to solve the problem incrementally
+ * simulated dataset This version uses iSAM2 to solve the problem incrementally
  * @author  Duy-Nguyen Ta
  */
 
@@ -25,27 +25,28 @@
 // For loading the data
 #include "SFMdata.h"
 
-// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
+// Camera observations of landmarks will be stored as Point2 (x, y).
 #include <gtsam/geometry/Point2.h>
 
-// Each variable in the system (poses and landmarks) must be identified with a unique key.
+// Each variable in the system (poses and landmarks) must be identified with a
-// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
+// unique key. We can either use simple integer keys (1, 2, 3, ...) or symbols
-// Here we will use Symbols
+// (X1, X2, L1). Here we will use Symbols
 #include <gtsam/inference/Symbol.h>
 
-// We want to use iSAM2 to solve the structure-from-motion problem incrementally, so
+// We want to use iSAM2 to solve the structure-from-motion problem
-// include iSAM2 here
+// incrementally, so include iSAM2 here
 #include <gtsam/nonlinear/ISAM2.h>
 
-// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,
+// iSAM2 requires as input a set of new factors to be added stored in a factor
-// and initial guesses for any new variables used in the added factors
+// graph, and initial guesses for any new variables used in the added factors
 #include <gtsam/nonlinear/NonlinearFactorGraph.h>
 #include <gtsam/nonlinear/Values.h>
 
-// In GTSAM, measurement functions are represented as 'factors'. Several common factors
+// In GTSAM, measurement functions are represented as 'factors'. Several common
-// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
+// factors have been provided with the library for solving robotics/SLAM/Bundle
-// Here we will use Projection factors to model the camera's landmark observations.
+// Adjustment problems. Here we will use Projection factors to model the
-// Also, we will initialize the robot at some location using a Prior factor.
+// camera's landmark observations. Also, we will initialize the robot at some
+// location using a Prior factor.
 #include <gtsam/slam/PriorFactor.h>
 #include <gtsam/slam/ProjectionFactor.h>
 
@@ -56,12 +57,11 @@ using namespace gtsam;
 
 /* ************************************************************************* */
 int main(int argc, char* argv[]) {
-
   // Define the camera calibration parameters
   Cal3_S2::shared_ptr K(new Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
 
-  // Define the camera observation noise model
+  // Define the camera observation noise model, 1 pixel stddev
-  noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
+  auto measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0);
 
   // Create the set of ground-truth landmarks
   vector<Point3> points = createPoints();
@@ -69,10 +69,12 @@ int main(int argc, char* argv[]) {
   // Create the set of ground-truth poses
   vector<Pose3> poses = createPoses();
 
-  // Create an iSAM2 object. Unlike iSAM1, which performs periodic batch steps to maintain proper linearization
+  // Create an iSAM2 object. Unlike iSAM1, which performs periodic batch steps
-  // and efficient variable ordering, iSAM2 performs partial relinearization/reordering at each step. A parameter
+  // to maintain proper linearization and efficient variable ordering, iSAM2
-  // structure is available that allows the user to set various properties, such as the relinearization threshold
+  // performs partial relinearization/reordering at each step. A parameter
-  // and type of linear solver. For this example, we we set the relinearization threshold small so the iSAM2 result
+  // structure is available that allows the user to set various properties, such
+  // as the relinearization threshold and type of linear solver. For this
+  // example, we we set the relinearization threshold small so the iSAM2 result
   // will approach the batch result.
   ISAM2Params parameters;
   parameters.relinearizeThreshold = 0.01;
@@ -83,44 +85,52 @@ int main(int argc, char* argv[]) {
   NonlinearFactorGraph graph;
   Values initialEstimate;
 
-  // Loop over the different poses, adding the observations to iSAM incrementally
+  // Loop over the poses, adding the observations to iSAM incrementally
   for (size_t i = 0; i < poses.size(); ++i) {
-
     // Add factors for each landmark observation
     for (size_t j = 0; j < points.size(); ++j) {
       SimpleCamera camera(poses[i], *K);
       Point2 measurement = camera.project(points[j]);
-      graph.emplace_shared<GenericProjectionFactor<Pose3, Point3, Cal3_S2> >(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), K);
+      graph.emplace_shared<GenericProjectionFactor<Pose3, Point3, Cal3_S2> >(
+          measurement, measurementNoise, Symbol('x', i), Symbol('l', j), K);
     }
 
     // Add an initial guess for the current pose
     // Intentionally initialize the variables off from the ground truth
-    initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
+    static Pose3 kDeltaPose(Rot3::Rodrigues(-0.1, 0.2, 0.25),
+                            Point3(0.05, -0.10, 0.20));
+    initialEstimate.insert(Symbol('x', i), poses[i] * kDeltaPose);
 
-    // If this is the first iteration, add a prior on the first pose to set the coordinate frame
+    // If this is the first iteration, add a prior on the first pose to set the
-    // and a prior on the first landmark to set the scale
+    // coordinate frame and a prior on the first landmark to set the scale Also,
-    // Also, as iSAM solves incrementally, we must wait until each is observed at least twice before
+    // as iSAM solves incrementally, we must wait until each is observed at
-    // adding it to iSAM.
+    // least twice before adding it to iSAM.
-    if( i == 0) {
+    if (i == 0) {
-      // Add a prior on pose x0
+      // Add a prior on pose x0, 30cm std on x,y,z and 0.1 rad on roll,pitch,yaw
-      noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(0.3),Vector3::Constant(0.1)).finished()); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
+      static auto kPosePrior = noiseModel::Diagonal::Sigmas(
-      graph.emplace_shared<PriorFactor<Pose3> >(Symbol('x', 0), poses[0], poseNoise);
+          (Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1))
+              .finished());
+      graph.emplace_shared<PriorFactor<Pose3> >(Symbol('x', 0), poses[0],
+                                                kPosePrior);
 
       // Add a prior on landmark l0
-      noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
+      static auto kPointPrior = noiseModel::Isotropic::Sigma(3, 0.1);
-      graph.emplace_shared<PriorFactor<Point3> >(Symbol('l', 0), points[0], pointNoise); // add directly to graph
+      graph.emplace_shared<PriorFactor<Point3> >(Symbol('l', 0), points[0],
+                                                 kPointPrior);
 
       // Add initial guesses to all observed landmarks
       // Intentionally initialize the variables off from the ground truth
+      static Point3 kDeltaPoint(-0.25, 0.20, 0.15);
       for (size_t j = 0; j < points.size(); ++j)
-        initialEstimate.insert<Point3>(Symbol('l', j), points[j] +Point3(-0.25, 0.20, 0.15));
+        initialEstimate.insert<Point3>(Symbol('l', j), points[j] + kDeltaPoint);
 
     } else {
       // Update iSAM with the new factors
       isam.update(graph, initialEstimate);
-      // Each call to iSAM2 update(*) performs one iteration of the iterative nonlinear solver.
+      // Each call to iSAM2 update(*) performs one iteration of the iterative
-      // If accuracy is desired at the expense of time, update(*) can be called additional times
+      // nonlinear solver. If accuracy is desired at the expense of time,
-      // to perform multiple optimizer iterations every step.
+      // update(*) can be called additional times to perform multiple optimizer
+      // iterations every step.
       isam.update();
       Values currentEstimate = isam.calculateEstimate();
       cout << "****************************************************" << endl;