119 lines
5.2 KiB
C++
119 lines
5.2 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file SFMExample.cpp
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* @brief A structure-from-motion problem on a simulated dataset
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* @author Duy-Nguyen Ta
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*/
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/**
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* A structure-from-motion example with landmarks
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* - The landmarks form a 10 meter cube
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* - The robot rotates around the landmarks, always facing towards the cube
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*/
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// For loading the data
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#include "SFMdata.h"
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// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
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#include <gtsam/geometry/Point2.h>
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// Each variable in the system (poses and landmarks) must be identified with a unique key.
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// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
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// Here we will use Symbols
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#include <gtsam/inference/Symbol.h>
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// In GTSAM, measurement functions are represented as 'factors'. Several common factors
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// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
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// Here we will use Projection factors to model the camera's landmark observations.
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// Also, we will initialize the robot at some location using a Prior factor.
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/ProjectionFactor.h>
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// When the factors are created, we will add them to a Factor Graph. As the factors we are using
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// are nonlinear factors, we will need a Nonlinear Factor Graph.
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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// Finally, once all of the factors have been added to our factor graph, we will want to
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// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values.
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// GTSAM includes several nonlinear optimizers to perform this step. Here we will use a
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// trust-region method known as Powell's Degleg
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#include <gtsam/nonlinear/DoglegOptimizer.h>
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// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
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// nonlinear functions around an initial linearization point, then solve the linear system
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// to update the linearization point. This happens repeatedly until the solver converges
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// to a consistent set of variable values. This requires us to specify an initial guess
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// for each variable, held in a Values container.
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#include <gtsam/nonlinear/Values.h>
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#include <vector>
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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int main(int argc, char* argv[]) {
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// Define the camera calibration parameters
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Cal3_S2::shared_ptr K(new Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
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// Define the camera observation noise model
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noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
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// Create the set of ground-truth landmarks
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vector<Point3> points = createPoints();
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// Create the set of ground-truth poses
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vector<Pose3> poses = createPoses();
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// Create a factor graph
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NonlinearFactorGraph graph;
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// Add a prior on pose x1. This indirectly specifies where the origin is.
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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
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graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise)); // add directly to graph
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// Simulated measurements from each camera pose, adding them to the factor graph
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for (size_t i = 0; i < poses.size(); ++i) {
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for (size_t j = 0; j < points.size(); ++j) {
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SimpleCamera camera(poses[i], *K);
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Point2 measurement = camera.project(points[j]);
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graph.push_back(GenericProjectionFactor<Pose3, Point3, Cal3_S2>(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), K));
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}
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}
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// Because the structure-from-motion problem has a scale ambiguity, the problem is still under-constrained
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// Here we add a prior on the position of the first landmark. This fixes the scale by indicating the distance
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// between the first camera and the first landmark. All other landmark positions are interpreted using this scale.
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noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
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graph.push_back(PriorFactor<Point3>(Symbol('l', 0), points[0], pointNoise)); // add directly to graph
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graph.print("Factor Graph:\n");
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// Create the data structure to hold the initial estimate to the solution
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// Intentionally initialize the variables off from the ground truth
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Values initialEstimate;
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for (size_t i = 0; i < poses.size(); ++i)
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initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
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for (size_t j = 0; j < points.size(); ++j)
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initialEstimate.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
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initialEstimate.print("Initial Estimates:\n");
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/* Optimize the graph and print results */
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Values result = DoglegOptimizer(graph, initialEstimate).optimize();
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result.print("Final results:\n");
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return 0;
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}
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/* ************************************************************************* */
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