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use ExpressionFactorGraph

release/4.3a0
dellaert 2014-12-13 08:35:22 +01:00
parent 07177662f2
commit 2225c10fc0
1 changed files with 18 additions and 18 deletions
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36
gtsam_unstable/examples/SFMExampleExpressions.cpp
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@ -24,14 +24,11 @@
// The two new headers that allow using our Automatic Differentiation Expression framework
#include <gtsam_unstable/slam/expressions.h>
#include <gtsam_unstable/nonlinear/ExpressionFactor.h>
#include <gtsam_unstable/nonlinear/ExpressionFactorGraph.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>
@ -40,27 +37,29 @@
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);
noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
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
NonlinearFactorGraph graph;
ExpressionFactorGraph 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());
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
// The object x0 is an Expression, and we create a factor wanting it to be equal to poses[0]
// 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));
graph.addExpressionFactor(x0, poses[0], poseNoise);
// We create a constant Expression for the calibration here
Cal3_S2_ cK(K);
@ -74,25 +73,26 @@ int main(int argc, char* argv[]) {
// 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));
// Again, here we use an ExpressionFactor
graph.addExpressionFactor(prediction, measurement, measurementNoise);
}
}
// 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)));
Isotropic::shared_ptr pointNoise = Isotropic::Sigma(3, 0.1);
graph.addExpressionFactor(Point3_('l', 0), points[0], pointNoise);
// Create perturbed initial
Values initialEstimate;
Values initial;
Pose3 delta(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20));
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))));
initial.insert(Symbol('x', i), poses[i].compose(delta));
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;
initial.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
cout << "initial error = " << graph.error(initial) << endl;
/* Optimize the graph and print results */
Values result = DoglegOptimizer(graph, initialEstimate).optimize();
Values result = DoglegOptimizer(graph, initial).optimize();
cout << "final error = " << graph.error(result) << endl;
return 0;