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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 SFMExampleExpressions.cpp
* @brief A structure-from-motion example done with Expressions
* @author Frank Dellaert
* @author Duy-Nguyen Ta
* @date October 1, 2014
*/
/**
* This is the Expression version of SFMExample
* See detailed description of headers there, this focuses on explaining the AD part
*/
// The two new headers that allow using our Automatic Differentiation Expression framework
#include <gtsam/slam/expressions.h>
#include <gtsam/nonlinear/ExpressionFactorGraph.h>
// Header order is close to far
#include "SFMdata.h"
#include <gtsam/geometry/Point2.h>
#include <gtsam/nonlinear/DoglegOptimizer.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/inference/Symbol.h>
#include <vector>
using namespace std;
using namespace gtsam;
using namespace noiseModel;
/* ************************************************************************* */
int main(int argc, char* argv[]) {
Cal3_S2 K(50.0, 50.0, 0.0, 50.0, 50.0);
Isotropic::shared_ptr measurementNoise = Isotropic::Sigma(2, 1.0); // one pixel in u and v
// Create the set of ground-truth landmarks and poses
vector<Point3> points = createPoints();
vector<Pose3> poses = createPoses();
// Create a factor graph
ExpressionFactorGraph graph;
// Specify uncertainty on first pose prior
Vector6 sigmas; sigmas << Vector3(0.3,0.3,0.3), Vector3(0.1,0.1,0.1);
Diagonal::shared_ptr poseNoise = Diagonal::Sigmas(sigmas);
// Here we don't use a PriorFactor but directly the ExpressionFactor class
// x0 is an Expression, and we create a factor wanting it to be equal to poses[0]
Pose3_ x0('x',0);
graph.addExpressionFactor(x0, poses[0], poseNoise);
// We create a constant Expression for the calibration here
Cal3_S2_ cK(K);
// Simulated measurements from each camera pose, adding them to the factor graph
for (size_t i = 0; i < poses.size(); ++i) {
Pose3_ x('x', i);
PinholeCamera<Cal3_S2> camera(poses[i], K);
for (size_t j = 0; j < points.size(); ++j) {
Point2 measurement = camera.project(points[j]);
// Below an expression for the prediction of the measurement:
Point3_ p('l', j);
Point2_ prediction = uncalibrate(cK, project(transformTo(x, p)));
// Again, here we use an ExpressionFactor
graph.addExpressionFactor(prediction, measurement, measurementNoise);
}
}
// Add prior on first point to constrain scale, again with ExpressionFactor
Isotropic::shared_ptr pointNoise = Isotropic::Sigma(3, 0.1);
graph.addExpressionFactor(Point3_('l', 0), points[0], pointNoise);
// Create perturbed initial
Values initial;
Pose3 delta(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20));
for (size_t i = 0; i < poses.size(); ++i)
initial.insert(Symbol('x', i), poses[i].compose(delta));
for (size_t j = 0; j < points.size(); ++j)
initial.insert<Point3>(Symbol('l', j), points[j] + Point3(-0.25, 0.20, 0.15));
cout << "initial error = " << graph.error(initial) << endl;
/* Optimize the graph and print results */
Values result = DoglegOptimizer(graph, initial).optimize();
cout << "final error = " << graph.error(result) << endl;
return 0;
}
/* ************************************************************************* */
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