add a smart factor sfm example

examples/SFMExample_SmartFactor.cpp

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+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file    SFMExample_SmartFactor.cpp
+ * @brief   A structure-from-motion problem on a simulated dataset, using smart projection factor
+ * @author  Duy-Nguyen Ta
+ * @author  Jing Dong
+ */
+
+/**
+ * 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>
+
+// Each variable in the system (poses and landmarks) 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/inference/Symbol.h>
+
+// In GTSAM, measurement functions are represented as 'factors'.
+// The factor we used here is SmartProjectionPoseFactor. Every smart factor represent a single landmark,
+// The SmartProjectionPoseFactor only optimize the pose of camera, not the calibration,
+// The calibration should be known.
+#include <gtsam/slam/SmartProjectionPoseFactor.h>
+
+// Also, we will initialize the robot at some location using a Prior factor.
+#include <gtsam/slam/PriorFactor.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;
+
+// Make the typename short so it looks much cleaner
+typedef gtsam::SmartProjectionPoseFactor<gtsam::Pose3, gtsam::Point3, gtsam::Cal3_S2>
+  SmartFactor;
+
+/* ************************************************************************* */
+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
+  noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
+
+  // Create the set of ground-truth landmarks
+  vector<Point3> points = createPoints();
+
+  // Create the set of ground-truth poses
+  vector<Pose3> poses = createPoses();
+
+  // Create a factor graph
+  NonlinearFactorGraph graph;
+
+  // Add a prior on pose x0. This indirectly specifies where the origin is.
+  noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1))); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
+  graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise)); // add directly to graph
+
+  // Simulated measurements from each camera pose, adding them to the factor graph
+  for (size_t i = 0; i < points.size(); ++i) {
+
+    // every landmark represent a single landmark, we use shared pointer to init the factor, and then insert measurements.
+    SmartFactor::shared_ptr smartfactor(new SmartFactor());
+
+    for (size_t j = 0; j < poses.size(); ++j) {
+
+      // generate the 2D measurement
+      SimpleCamera camera(poses[j], *K);
+      Point2 measurement = camera.project(points[i]);
+
+      // call add() function to add measurment into a single factor, here we need to add:
+      //    1. the 2D measurement
+      //    2. the corresponding camera's key
+      //    3. camera noise model
+      //    4. camera calibration
+      smartfactor->add(measurement, Symbol('x', j), measurementNoise, K);
+    }
+
+    // insert the smart factor in the graph
+    graph.push_back(smartfactor);
+  }
+
+  // Because the structure-from-motion problem has a scale ambiguity, the problem is still under-constrained
+  // Here we add a prior on the second pose x1, so this will fix the scale by indicating the distance between x0 and x1.
+  // Because these two are fixed, the rest poses will be alse fixed.
+  graph.push_back(PriorFactor<Pose3>(Symbol('x', 1), poses[1], poseNoise)); // add directly to graph
+
+  graph.print("Factor Graph:\n");
+
+  // Create the data structure to hold the initial estimate to the solution
+  // Intentionally initialize the variables off from the ground truth
+  Values initialEstimate;
+  for (size_t i = 0; i < poses.size(); ++i)
+    initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
+  initialEstimate.print("Initial Estimates:\n");
+
+  // Optimize the graph and print results
+  Values result = DoglegOptimizer(graph, initialEstimate).optimize();
+  result.print("Final results:\n");
+
+
+  return 0;
+}
+/* ************************************************************************* */
+