102 lines
3.8 KiB
C++
102 lines
3.8 KiB
C++
/* ----------------------------------------------------------------------------
|
|
|
|
* 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/ExpressionFactor.h>
|
|
|
|
// Header order is close to far
|
|
#include <examples/SFMdata.h>
|
|
#include <gtsam/slam/PriorFactor.h>
|
|
#include <gtsam/slam/ProjectionFactor.h>
|
|
#include <gtsam/geometry/Point2.h>
|
|
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
|
#include <gtsam/nonlinear/DoglegOptimizer.h>
|
|
#include <gtsam/nonlinear/Values.h>
|
|
#include <gtsam/inference/Symbol.h>
|
|
|
|
#include <vector>
|
|
|
|
using namespace std;
|
|
using namespace gtsam;
|
|
|
|
/* ************************************************************************* */
|
|
int main(int argc, char* argv[]) {
|
|
|
|
Cal3_S2 K(50.0, 50.0, 0.0, 50.0, 50.0);
|
|
noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::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
|
|
NonlinearFactorGraph graph;
|
|
|
|
// Specify uncertainty on first pose prior
|
|
noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1)).finished());
|
|
|
|
// Here we don't use a PriorFactor but directly the ExpressionFactor class
|
|
// The object x0 is an Expression, and we create a factor wanting it to be equal to poses[0]
|
|
Pose3_ x0('x',0);
|
|
graph.push_back(ExpressionFactor<Pose3>(poseNoise, poses[0], x0));
|
|
|
|
// 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);
|
|
SimpleCamera 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(transform_to(x, p)));
|
|
// Again, here we use a ExpressionFactor
|
|
graph.push_back(ExpressionFactor<Point2>(measurementNoise, measurement, prediction));
|
|
}
|
|
}
|
|
|
|
// Add prior on first point to constrain scale, again with ExpressionFactor
|
|
noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
|
|
graph.push_back(ExpressionFactor<Point3>(pointNoise, points[0], Point3_('l', 0)));
|
|
|
|
// Create perturbed initial
|
|
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))));
|
|
for (size_t j = 0; j < points.size(); ++j)
|
|
initialEstimate.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
|
|
cout << "initial error = " << graph.error(initialEstimate) << endl;
|
|
|
|
/* Optimize the graph and print results */
|
|
Values result = DoglegOptimizer(graph, initialEstimate).optimize();
|
|
cout << "final error = " << graph.error(result) << endl;
|
|
|
|
return 0;
|
|
}
|
|
/* ************************************************************************* */
|
|
|