130 lines
4.8 KiB
C++
130 lines
4.8 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 FisheyeExample.cpp
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* @brief A visualSLAM example for the structure-from-motion problem on a
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* simulated dataset. This version uses a fisheye camera model and a GaussNewton
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* solver to solve the graph in one batch
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* @author ghaggin
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* @Date Apr 9,2020
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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 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
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// unique key. We can either use simple integer keys (1, 2, 3, ...) or symbols
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// (X1, X2, L1). Here we will use Symbols
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#include <gtsam/inference/Symbol.h>
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// Use GaussNewtonOptimizer to solve graph
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#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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// In GTSAM, measurement functions are represented as 'factors'. Several common
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// factors have been provided with the library for solving robotics/SLAM/Bundle
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// Adjustment problems. Here we will use Projection factors to model the
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// camera's landmark observations. Also, we will initialize the robot at some
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// location using a Prior factor.
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#include <gtsam/geometry/Cal3Fisheye.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/ProjectionFactor.h>
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#include <fstream>
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#include <vector>
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using namespace std;
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using namespace gtsam;
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using symbol_shorthand::L; // for landmarks
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using symbol_shorthand::X; // for poses
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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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auto K = std::make_shared<Cal3Fisheye>(
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278.66, 278.48, 0.0, 319.75, 241.96, -0.013721808247486035,
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0.020727425669427896, -0.012786476702685545, 0.0025242267320687625);
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// Define the camera observation noise model, 1 pixel stddev
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auto measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0);
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// Create the set of ground-truth landmarks
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const vector<Point3> points = createPoints();
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// Create the set of ground-truth poses
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const vector<Pose3> poses = createPoses();
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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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// Add a prior on pose x0, 0.1 rad on roll,pitch,yaw, and 30cm std on x,y,z
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auto posePrior = noiseModel::Diagonal::Sigmas(
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(Vector(6) << Vector3::Constant(0.1), Vector3::Constant(0.3)).finished());
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graph.emplace_shared<PriorFactor<Pose3>>(X(0), poses[0], posePrior);
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// Add a prior on landmark l0
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auto pointPrior = noiseModel::Isotropic::Sigma(3, 0.1);
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graph.emplace_shared<PriorFactor<Point3>>(L(0), points[0], pointPrior);
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// Add initial guesses to all observed landmarks
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// Intentionally initialize the variables off from the ground truth
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static const Point3 kDeltaPoint(-0.25, 0.20, 0.15);
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for (size_t j = 0; j < points.size(); ++j)
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initialEstimate.insert<Point3>(L(j), points[j] + kDeltaPoint);
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// Loop over the poses, adding the observations to the graph
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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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PinholeCamera<Cal3Fisheye> camera(poses[i], *K);
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Point2 measurement = camera.project(points[j]);
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graph.emplace_shared<GenericProjectionFactor<Pose3, Point3, Cal3Fisheye>>(
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measurement, measurementNoise, X(i), L(j), K);
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}
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// Add an initial guess for the current pose
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// Intentionally initialize the variables off from the ground truth
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static const Pose3 kDeltaPose(Rot3::Rodrigues(-0.1, 0.2, 0.25),
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Point3(0.05, -0.10, 0.20));
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initialEstimate.insert(X(i), poses[i] * kDeltaPose);
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}
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GaussNewtonParams params;
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params.setVerbosity("TERMINATION");
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params.maxIterations = 10000;
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std::cout << "Optimizing the factor graph" << std::endl;
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GaussNewtonOptimizer optimizer(graph, initialEstimate, params);
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Values result = optimizer.optimize();
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std::cout << "Optimization complete" << std::endl;
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std::cout << "initial error=" << graph.error(initialEstimate) << std::endl;
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std::cout << "final error=" << graph.error(result) << std::endl;
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graph.saveGraph("examples/vio_batch.dot", result);
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return 0;
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}
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/* ************************************************************************* */
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