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