128 lines
4.4 KiB
C++
128 lines
4.4 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 testVisualISAM2.cpp
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* @brief Test convergence of visualSLAM example.
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* @author Duy-Nguyen Ta
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* @author Frank Dellaert
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*/
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#include <CppUnitLite/TestHarness.h>
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#include <examples/SFMdata.h>
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#include <gtsam/geometry/Point2.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/ProjectionFactor.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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TEST(testVisualISAM2, all)
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{
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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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auto measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0);
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// Create ground truth data
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vector<Point3> points = createPoints();
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vector<Pose3> poses = createPoses();
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// Set the parameters
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ISAM2Params parameters;
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parameters.relinearizeThreshold = 0.01;
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parameters.relinearizeSkip = 1;
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ISAM2 isam(parameters);
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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 poses, adding the observations to iSAM incrementally
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for (size_t i = 0; i < poses.size(); ++i)
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{
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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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{
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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>>(
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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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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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// Treat first iteration as special case
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if (i == 0)
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{
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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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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] + kDeltaPoint);
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}
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else
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{
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// Update iSAM with the new factors
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isam.update(graph, initialEstimate);
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// Do an extra update to converge withing these 8 iterations
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isam.update();
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// Optimize
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Values currentEstimate = isam.calculateEstimate();
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// reset for next iteration
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graph.resize(0);
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initialEstimate.clear();
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}
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} // for loop
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auto result = isam.calculateEstimate();
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EXPECT_LONGS_EQUAL(16, result.size());
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for (size_t j = 0; j < points.size(); ++j)
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{
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Symbol key('l', j);
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EXPECT(assert_equal(points[j], result.at<Point3>(key), 0.01));
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}
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}
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/* ************************************************************************* */
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int main()
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{
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */
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