143 lines
5.5 KiB
C++
143 lines
5.5 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 VisualISAMExample.cpp
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* @brief A visualSLAM example for the structure-from-motion problem on a simulated dataset
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* This version uses iSAM to solve the problem incrementally
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* @author Duy-Nguyen Ta
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* @author Frank Dellaert
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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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// We want to use iSAM to solve the structure-from-motion problem incrementally, so
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// include iSAM here
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#include <gtsam/nonlinear/NonlinearISAM.h>
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// iSAM requires as input a set set of new factors to be added stored in a factor graph,
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// and initial guesses for any new variables used in the added factors
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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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 noise = //
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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 NonlinearISAM object which will relinearize and reorder the variables
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// every "relinearizeInterval" updates
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int relinearizeInterval = 3;
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NonlinearISAM isam(relinearizeInterval);
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// Create a Factor Graph and Values to hold the new data
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NonlinearFactorGraph graph;
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Values initialEstimate;
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// Loop over the different poses, adding the observations to iSAM incrementally
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for (size_t i = 0; i < poses.size(); ++i) {
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// Add factors for each landmark observation
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for (size_t j = 0; j < points.size(); ++j) {
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// Create ground truth measurement
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SimpleCamera camera(poses[i], *K);
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Point2 measurement = camera.project(points[j]);
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// Add measurement
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graph.emplace_shared<GenericProjectionFactor<Pose3, Point3, Cal3_S2> >(measurement, noise,
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Symbol('x', i), Symbol('l', j), K);
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}
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// Intentionally initialize the variables off from the ground truth
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Pose3 noise(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20));
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Pose3 initial_xi = poses[i].compose(noise);
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// Add an initial guess for the current pose
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initialEstimate.insert(Symbol('x', i), initial_xi);
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// If this is the first iteration, add a prior on the first pose to set the coordinate frame
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// and a prior on the first landmark to set the scale
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// Also, as iSAM solves incrementally, we must wait until each is observed at least twice before
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// adding it to iSAM.
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if (i == 0) {
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// Add a prior on pose x0, with 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas(
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(Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1)).finished());
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graph.emplace_shared<PriorFactor<Pose3> >(Symbol('x', 0), poses[0], poseNoise);
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// Add a prior on landmark l0
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noiseModel::Isotropic::shared_ptr pointNoise =
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noiseModel::Isotropic::Sigma(3, 0.1);
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graph.emplace_shared<PriorFactor<Point3> >(Symbol('l', 0), points[0], pointNoise);
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// Add initial guesses to all observed landmarks
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Point3 noise(-0.25, 0.20, 0.15);
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for (size_t j = 0; j < points.size(); ++j) {
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// Intentionally initialize the variables off from the ground truth
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Point3 initial_lj = points[j] + noise;
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initialEstimate.insert(Symbol('l', j), initial_lj);
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}
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} else {
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// Update iSAM with the new factors
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isam.update(graph, initialEstimate);
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Values currentEstimate = isam.estimate();
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cout << "****************************************************" << endl;
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cout << "Frame " << i << ": " << endl;
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currentEstimate.print("Current estimate: ");
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// Clear the factor graph and values for the next iteration
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graph.resize(0);
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initialEstimate.clear();
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}
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}
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return 0;
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}
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/* ************************************************************************* */
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