diff --git a/examples/SFMExample_SmartFactor.cpp b/examples/SFMExample_SmartFactor.cpp new file mode 100644 index 000000000..a96edd270 --- /dev/null +++ b/examples/SFMExample_SmartFactor.cpp @@ -0,0 +1,139 @@ +/* ---------------------------------------------------------------------------- + + * 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 SFMExample_SmartFactor.cpp + * @brief A structure-from-motion problem on a simulated dataset, using smart projection factor + * @author Duy-Nguyen Ta + * @author Jing Dong + */ + +/** + * 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 + +// 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 + +// In GTSAM, measurement functions are represented as 'factors'. +// The factor we used here is SmartProjectionPoseFactor. Every smart factor represent a single landmark, +// The SmartProjectionPoseFactor only optimize the pose of camera, not the calibration, +// The calibration should be known. +#include + +// Also, we will initialize the robot at some location using a Prior factor. +#include + +// When the factors are created, we will add them to a Factor Graph. As the factors we are using +// are nonlinear factors, we will need a Nonlinear Factor Graph. +#include + +// Finally, once all of the factors have been added to our factor graph, we will want to +// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values. +// GTSAM includes several nonlinear optimizers to perform this step. Here we will use a +// trust-region method known as Powell's Degleg +#include + +// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the +// nonlinear functions around an initial linearization point, then solve the linear system +// to update the linearization point. This happens repeatedly until the solver converges +// to a consistent set of variable values. This requires us to specify an initial guess +// for each variable, held in a Values container. +#include + +#include + +using namespace std; +using namespace gtsam; + +// Make the typename short so it looks much cleaner +typedef gtsam::SmartProjectionPoseFactor + SmartFactor; + +/* ************************************************************************* */ +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 + noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v + + // Create the set of ground-truth landmarks + vector points = createPoints(); + + // Create the set of ground-truth poses + vector poses = createPoses(); + + // Create a factor graph + NonlinearFactorGraph graph; + + // Add a prior on pose x0. This indirectly specifies where the origin is. + noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1))); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw + graph.push_back(PriorFactor(Symbol('x', 0), poses[0], poseNoise)); // add directly to graph + + // Simulated measurements from each camera pose, adding them to the factor graph + for (size_t i = 0; i < points.size(); ++i) { + + // every landmark represent a single landmark, we use shared pointer to init the factor, and then insert measurements. + SmartFactor::shared_ptr smartfactor(new SmartFactor()); + + for (size_t j = 0; j < poses.size(); ++j) { + + // generate the 2D measurement + SimpleCamera camera(poses[j], *K); + Point2 measurement = camera.project(points[i]); + + // call add() function to add measurment into a single factor, here we need to add: + // 1. the 2D measurement + // 2. the corresponding camera's key + // 3. camera noise model + // 4. camera calibration + smartfactor->add(measurement, Symbol('x', j), measurementNoise, K); + } + + // insert the smart factor in the graph + graph.push_back(smartfactor); + } + + // Because the structure-from-motion problem has a scale ambiguity, the problem is still under-constrained + // Here we add a prior on the second pose x1, so this will fix the scale by indicating the distance between x0 and x1. + // Because these two are fixed, the rest poses will be alse fixed. + graph.push_back(PriorFactor(Symbol('x', 1), poses[1], poseNoise)); // add directly to graph + + graph.print("Factor Graph:\n"); + + // Create the data structure to hold the initial estimate to the solution + // Intentionally initialize the variables off from the ground truth + 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)))); + initialEstimate.print("Initial Estimates:\n"); + + // Optimize the graph and print results + Values result = DoglegOptimizer(graph, initialEstimate).optimize(); + result.print("Final results:\n"); + + + return 0; +} +/* ************************************************************************* */ +