160 lines
6.9 KiB
C++
160 lines
6.9 KiB
C++
/* ----------------------------------------------------------------------------
|
|
|
|
* 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 VisualISAM2Example.cpp
|
|
* @brief A visualSLAM example for the structure-from-motion problem on a simulated dataset
|
|
* This version uses iSAM2 to solve the problem incrementally
|
|
* @author Duy-Nguyen Ta
|
|
*/
|
|
|
|
/**
|
|
* 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
|
|
*/
|
|
|
|
// As this is a full 3D problem, we will use Pose3 variables to represent the camera
|
|
// positions and Point3 variables (x, y, z) to represent the landmark coordinates.
|
|
// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
|
|
// We will also need a camera object to hold calibration information and perform projections.
|
|
#include <gtsam/geometry/Pose3.h>
|
|
#include <gtsam/geometry/Point3.h>
|
|
#include <gtsam/geometry/Point2.h>
|
|
#include <gtsam/geometry/SimpleCamera.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/nonlinear/Symbol.h>
|
|
|
|
// We want to use iSAM2 to solve the structure-from-motion problem incrementally, so
|
|
// include iSAM2 here
|
|
#include <gtsam/nonlinear/ISAM2.h>
|
|
|
|
// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,
|
|
// and initial guesses for any new variables used in the added factors
|
|
#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/slam/PriorFactor.h>
|
|
#include <gtsam/slam/ProjectionFactor.h>
|
|
|
|
#include <vector>
|
|
|
|
using namespace std;
|
|
using namespace gtsam;
|
|
|
|
/* ************************************************************************* */
|
|
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
|
|
std::vector<gtsam::Point3> points;
|
|
points.push_back(gtsam::Point3(10.0,10.0,10.0));
|
|
points.push_back(gtsam::Point3(-10.0,10.0,10.0));
|
|
points.push_back(gtsam::Point3(-10.0,-10.0,10.0));
|
|
points.push_back(gtsam::Point3(10.0,-10.0,10.0));
|
|
points.push_back(gtsam::Point3(10.0,10.0,-10.0));
|
|
points.push_back(gtsam::Point3(-10.0,10.0,-10.0));
|
|
points.push_back(gtsam::Point3(-10.0,-10.0,-10.0));
|
|
points.push_back(gtsam::Point3(10.0,-10.0,-10.0));
|
|
|
|
// Create the set of ground-truth poses
|
|
std::vector<gtsam::Pose3> poses;
|
|
double radius = 30.0;
|
|
int i = 0;
|
|
double theta = 0.0;
|
|
gtsam::Point3 up(0,0,1);
|
|
gtsam::Point3 target(0,0,0);
|
|
for(; i < 8; ++i, theta += 2*M_PI/8) {
|
|
gtsam::Point3 position = Point3(radius*cos(theta), radius*sin(theta), 0.0);
|
|
gtsam::SimpleCamera camera = SimpleCamera::Lookat(position, target, up);
|
|
poses.push_back(camera.pose());
|
|
}
|
|
|
|
// Create an iSAM2 object. Unlike iSAM1, which performs periodic batch steps to maintain proper linearization
|
|
// and efficient variable ordering, iSAM2 performs partial relinearization/reordering at each step. A parameter
|
|
// structure is available that allows the user to set various properties, such as the relinearization threshold
|
|
// and type of linear solver. For this example, we we set the relinearization threshold small so the iSAM2 result
|
|
// will approach the batch result.
|
|
ISAM2Params parameters;
|
|
parameters.relinearizeThreshold = 0.01;
|
|
parameters.relinearizeSkip = 1;
|
|
ISAM2 isam(parameters);
|
|
|
|
// Create a Factor Graph and Values to hold the new data
|
|
NonlinearFactorGraph graph;
|
|
Values initialEstimate;
|
|
|
|
// Loop over the different poses, adding the observations to iSAM incrementally
|
|
for (size_t i = 0; i < poses.size(); ++i) {
|
|
|
|
// Add factors for each landmark observation
|
|
for (size_t j = 0; j < points.size(); ++j) {
|
|
SimpleCamera camera(poses[i], *K);
|
|
Point2 measurement = camera.project(points[j]);
|
|
graph.add(GenericProjectionFactor<Pose3, Point3, Cal3_S2>(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), K));
|
|
}
|
|
|
|
// Add an initial guess for the current pose
|
|
// Intentionally initialize the variables off from the ground truth
|
|
initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
|
|
|
|
// If this is the first iteration, add a prior on the first pose to set the coordinate frame
|
|
// and a prior on the first landmark to set the scale
|
|
// Also, as iSAM solves incrementally, we must wait until each is observed at least twice before
|
|
// adding it to iSAM.
|
|
if( i == 0) {
|
|
// Add a prior on pose x0
|
|
noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas(Vector_(6, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1)); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
|
|
graph.add(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise));
|
|
|
|
// Add a prior on landmark l0
|
|
noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
|
|
graph.add(PriorFactor<Point3>(Symbol('l', 0), points[0], pointNoise)); // add directly to graph
|
|
|
|
// Add initial guesses to all observed landmarks
|
|
// Intentionally initialize the variables off from the ground truth
|
|
for (size_t j = 0; j < points.size(); ++j)
|
|
initialEstimate.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
|
|
|
|
} else {
|
|
// Update iSAM with the new factors
|
|
isam.update(graph, initialEstimate);
|
|
// Each call to iSAM2 update(*) performs one iteration of the iterative nonlinear solver.
|
|
// If accuracy is desired at the expense of time, update(*) can be called additional times
|
|
// to perform multiple optimizer iterations every step.
|
|
isam.update();
|
|
Values currentEstimate = isam.calculateEstimate();
|
|
cout << "****************************************************" << endl;
|
|
cout << "Frame " << i << ": " << endl;
|
|
currentEstimate.print("Current estimate: ");
|
|
|
|
// Clear the factor graph and values for the next iteration
|
|
graph.resize(0);
|
|
initialEstimate.clear();
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
/* ************************************************************************* */
|