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

// 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'. 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>

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

// iSAM requires as input a set 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>

#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
  auto noise = 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 NonlinearISAM object which will relinearize and reorder the variables
  // every "relinearizeInterval" updates
  int relinearizeInterval = 3;
  NonlinearISAM isam(relinearizeInterval);

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

  // Loop over the different 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) {
      // Create ground truth measurement
      PinholeCamera<Cal3_S2> camera(poses[i], *K);
      Point2 measurement = camera.project(points[j]);
      // Add measurement
      graph.emplace_shared<GenericProjectionFactor<Pose3, Point3, Cal3_S2> >(measurement, noise,
              Symbol('x', i), Symbol('l', j), K);
    }

    // Intentionally initialize the variables off from the ground truth
    Pose3 noise(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20));
    Pose3 initial_xi = poses[i].compose(noise);

    // Add an initial guess for the current pose
    initialEstimate.insert(Symbol('x', i), initial_xi);

    // 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, with 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
      auto poseNoise = noiseModel::Diagonal::Sigmas(
          (Vector(6) << Vector3::Constant(0.1), Vector3::Constant(0.3)).finished());
      graph.addPrior(Symbol('x', 0), poses[0], poseNoise);

      // Add a prior on landmark l0
      auto pointNoise =
          noiseModel::Isotropic::Sigma(3, 0.1);
      graph.addPrior(Symbol('l', 0), points[0], pointNoise);

      // Add initial guesses to all observed landmarks
      Point3 noise(-0.25, 0.20, 0.15);
      for (size_t j = 0; j < points.size(); ++j) {
        // Intentionally initialize the variables off from the ground truth
        Point3 initial_lj = points[j] + noise;
        initialEstimate.insert(Symbol('l', j), initial_lj);
      }

    } else {
      // Update iSAM with the new factors
      isam.update(graph, initialEstimate);
      Values currentEstimate = isam.estimate();
      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;
}
/* ************************************************************************* */