env/gtsam-4.1.1/examples/VisualISAM2Example.cpp

/* ----------------------------------------------------------------------------

 * 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    VisualISAM2Example.cpp
 * @brief   A visualSLAM example for the structure-from-motion problem on a
 * simulated dataset This version uses iSAM2 to solve the problem incrementally
 * @author  Duy-Nguyen Ta
 */

/**
 * 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 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>

// We want to use iSAM2 to solve the structure-from-motion problem
// incrementally, so include iSAM2 here
#include <gtsam/nonlinear/ISAM2.h>

// iSAM2 requires as input a set of new factors to be added stored in a factor
// 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 have been provided with the library for solving robotics/SLAM/Bundle
// Adjustment problems. Here we will use Projection factors to model the
// camera's landmark observations. Also, we will initialize the robot at some
// location using a Prior factor.
#include <gtsam/slam/ProjectionFactor.h>

#include <vector>

using namespace std;
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, 1 pixel stddev
  auto measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0);

  // Create the set of ground-truth landmarks
  vector<Point3> points = createPoints();

  // 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 and efficient variable ordering, iSAM2
  // performs partial relinearization/reordering at each step. A parameter
  // 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;
  parameters.relinearizeSkip = 1;
  ISAM2 isam(parameters);

  // Create a Factor Graph and Values to hold the new data
  NonlinearFactorGraph graph;
  Values initialEstimate;

  // 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) {
      PinholeCamera<Cal3_S2> 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);
    }

    // Add an initial guess for the current pose
    // Intentionally initialize the variables off from the ground truth
    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 and a prior on the first landmark to set the scale Also,
    // as iSAM solves incrementally, we must wait until each is observed at
    // least twice before adding it to iSAM.
    if (i == 0) {
      // Add a prior on pose x0, 30cm std on x,y,z and 0.1 rad on roll,pitch,yaw
      static auto kPosePrior = noiseModel::Diagonal::Sigmas(
          (Vector(6) << Vector3::Constant(0.1), Vector3::Constant(0.3))
              .finished());
      graph.addPrior(Symbol('x', 0), poses[0], kPosePrior);

      // Add a prior on landmark l0
      static auto kPointPrior = noiseModel::Isotropic::Sigma(3, 0.1);
      graph.addPrior(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] + 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. If accuracy is desired at the expense of time,
      // update(*) can be called additional times to perform multiple optimizer
      // iterations every step.
      isam.update();
      Values currentEstimate = isam.calculateEstimate();
      cout << "****************************************************" << endl;
      cout << "Frame " << i << ": " << endl;
      currentEstimate.print("Current estimate: ");

      // Clear the factor graph and values for the next iteration
      graph.resize(0);
      initialEstimate.clear();
    }
  }

  return 0;
}
/* ************************************************************************* */