Cleaned up example
Frank Dellaert
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3e0ee2052d
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efa35e6a82
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@ -11,8 +11,8 @@
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/**
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* @file VisualISAM2Example.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 iSAM2 to solve the problem incrementally
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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 iSAM2 to solve the problem incrementally
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* @author Duy-Nguyen Ta
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*/
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@ -25,27 +25,28 @@
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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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// 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 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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// 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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// We want to use iSAM2 to solve the structure-from-motion problem incrementally, so
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// include iSAM2 here
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// We want to use iSAM2 to solve the structure-from-motion problem
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// incrementally, so include iSAM2 here
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#include <gtsam/nonlinear/ISAM2.h>
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// iSAM2 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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// iSAM2 requires as input a set of new factors to be added stored in a factor
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// graph, 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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// 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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// 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/slam/PriorFactor.h>
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#include <gtsam/slam/ProjectionFactor.h>
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@ -56,12 +57,11 @@ 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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// 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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vector<Point3> points = createPoints();
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@ -69,10 +69,12 @@ int main(int argc, char* argv[]) {
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// Create the set of ground-truth poses
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vector<Pose3> poses = createPoses();
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// Create an iSAM2 object. Unlike iSAM1, which performs periodic batch steps to maintain proper linearization
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// and efficient variable ordering, iSAM2 performs partial relinearization/reordering at each step. A parameter
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// structure is available that allows the user to set various properties, such as the relinearization threshold
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// and type of linear solver. For this example, we we set the relinearization threshold small so the iSAM2 result
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// Create an iSAM2 object. Unlike iSAM1, which performs periodic batch steps
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// to maintain proper linearization and efficient variable ordering, iSAM2
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// performs partial relinearization/reordering at each step. A parameter
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// structure is available that allows the user to set various properties, such
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// as the relinearization threshold and type of linear solver. For this
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// example, we we set the relinearization threshold small so the iSAM2 result
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// will approach the batch result.
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ISAM2Params parameters;
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parameters.relinearizeThreshold = 0.01;
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@ -83,44 +85,52 @@ int main(int argc, char* argv[]) {
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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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// Loop over the 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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SimpleCamera camera(poses[i], *K);
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Point2 measurement = camera.project(points[j]);
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graph.emplace_shared<GenericProjectionFactor<Pose3, Point3, Cal3_S2> >(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), K);
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graph.emplace_shared<GenericProjectionFactor<Pose3, Point3, Cal3_S2> >(
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measurement, measurementNoise, Symbol('x', i), Symbol('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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initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
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static 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(Symbol('x', i), poses[i] * kDeltaPose);
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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
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(0.3),Vector3::Constant(0.1)).finished()); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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graph.emplace_shared<PriorFactor<Pose3> >(Symbol('x', 0), poses[0], poseNoise);
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// If this is the first iteration, add a prior on the first pose to set the
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// coordinate frame and a prior on the first landmark to set the scale Also,
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// as iSAM solves incrementally, we must wait until each is observed at
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// least twice before adding it to iSAM.
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if (i == 0) {
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// Add a prior on pose x0, 30cm std on x,y,z and 0.1 rad on roll,pitch,yaw
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static auto kPosePrior = noiseModel::Diagonal::Sigmas(
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(Vector(6) << Vector3::Constant(0.3), Vector3::Constant(0.1))
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.finished());
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graph.emplace_shared<PriorFactor<Pose3> >(Symbol('x', 0), poses[0],
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kPosePrior);
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// Add a prior on landmark l0
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noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
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graph.emplace_shared<PriorFactor<Point3> >(Symbol('l', 0), points[0], pointNoise); // add directly to graph
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static auto kPointPrior = noiseModel::Isotropic::Sigma(3, 0.1);
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graph.emplace_shared<PriorFactor<Point3> >(Symbol('l', 0), points[0],
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kPointPrior);
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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 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>(Symbol('l', j), points[j] +Point3(-0.25, 0.20, 0.15));
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initialEstimate.insert<Point3>(Symbol('l', j), points[j] + kDeltaPoint);
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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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// Each call to iSAM2 update(*) performs one iteration of the iterative nonlinear solver.
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// If accuracy is desired at the expense of time, update(*) can be called additional times
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// to perform multiple optimizer iterations every step.
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// Each call to iSAM2 update(*) performs one iteration of the iterative
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// nonlinear solver. If accuracy is desired at the expense of time,
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// update(*) can be called additional times to perform multiple optimizer
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// iterations every step.
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isam.update();
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Values currentEstimate = isam.calculateEstimate();
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cout << "****************************************************" << endl;
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