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/**
* @file testNonlinearISAM
* @author Alex Cunningham
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/sam/BearingRangeFactor.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/nonlinear/NonlinearEquality.h>
#include <gtsam/nonlinear/NonlinearISAM.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/linear/Sampler.h>
#include <gtsam/geometry/Pose2.h>
#include <iostream>
#include <sstream>
using namespace std;
using namespace gtsam;
const double tol=1e-5;
/* ************************************************************************* */
TEST(testNonlinearISAM, markov_chain ) {
int reorder_interval = 2;
NonlinearISAM isamChol(reorder_interval, EliminatePreferCholesky); // create an ISAM object
NonlinearISAM isamQR(reorder_interval, EliminateQR); // create an ISAM object
SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector3(3.0, 3.0, 0.5));
Sampler sampler(model, 42u);
// create initial graph
Pose2 cur_pose; // start at origin
NonlinearFactorGraph start_factors;
start_factors += NonlinearEquality<Pose2>(0, cur_pose);
Values init;
Values expected;
init.insert(0, cur_pose);
expected.insert(0, cur_pose);
isamChol.update(start_factors, init);
isamQR.update(start_factors, init);
// loop for a period of time to verify memory usage
size_t nrPoses = 21;
Pose2 z(1.0, 2.0, 0.1);
for (size_t i=1; i<=nrPoses; ++i) {
NonlinearFactorGraph new_factors;
new_factors += BetweenFactor<Pose2>(i-1, i, z, model);
Values new_init;
cur_pose = cur_pose.compose(z);
new_init.insert(i, cur_pose.retract(sampler.sample()));
expected.insert(i, cur_pose);
isamChol.update(new_factors, new_init);
isamQR.update(new_factors, new_init);
}
// verify values - all but the last one should be very close
Values actualChol = isamChol.estimate();
for (size_t i=0; i<nrPoses; ++i) {
EXPECT(assert_equal(expected.at<Pose2>(i), actualChol.at<Pose2>(i), tol));
}
Values actualQR = isamQR.estimate();
for (size_t i=0; i<nrPoses; ++i) {
EXPECT(assert_equal(expected.at<Pose2>(i), actualQR.at<Pose2>(i), tol));
}
}
/* ************************************************************************* */
TEST(testNonlinearISAM, markov_chain_with_disconnects ) {
int reorder_interval = 2;
NonlinearISAM isamChol(reorder_interval, EliminatePreferCholesky); // create an ISAM object
NonlinearISAM isamQR(reorder_interval, EliminateQR); // create an ISAM object
SharedDiagonal model3 = noiseModel::Diagonal::Sigmas(Vector3(3.0, 3.0, 0.5));
SharedDiagonal model2 = noiseModel::Diagonal::Sigmas(Vector2(2.0, 2.0));
Sampler sampler(model3, 42u);
// create initial graph
Pose2 cur_pose; // start at origin
NonlinearFactorGraph start_factors;
start_factors += NonlinearEquality<Pose2>(0, cur_pose);
Values init;
Values expected;
init.insert(0, cur_pose);
expected.insert(0, cur_pose);
isamChol.update(start_factors, init);
isamQR.update(start_factors, init);
size_t nrPoses = 21;
// create a constrained constellation of landmarks
Key lm1 = nrPoses+1, lm2 = nrPoses+2, lm3 = nrPoses+3;
Point2 landmark1(3., 4.), landmark2(6., 4.), landmark3(6., 9.);
expected.insert(lm1, landmark1);
expected.insert(lm2, landmark2);
expected.insert(lm3, landmark3);
// loop for a period of time to verify memory usage
Pose2 z(1.0, 2.0, 0.1);
for (size_t i=1; i<=nrPoses; ++i) {
NonlinearFactorGraph new_factors;
new_factors += BetweenFactor<Pose2>(i-1, i, z, model3);
Values new_init;
cur_pose = cur_pose.compose(z);
new_init.insert(i, cur_pose.retract(sampler.sample()));
expected.insert(i, cur_pose);
// Add a floating landmark constellation
if (i == 7) {
new_factors.addPrior(lm1, landmark1, model2);
new_factors.addPrior(lm2, landmark2, model2);
new_factors.addPrior(lm3, landmark3, model2);
// Initialize to origin
new_init.insert(lm1, Point2(0,0));
new_init.insert(lm2, Point2(0,0));
new_init.insert(lm3, Point2(0,0));
}
isamChol.update(new_factors, new_init);
isamQR.update(new_factors, new_init);
}
// verify values - all but the last one should be very close
Values actualChol = isamChol.estimate();
for (size_t i=0; i<nrPoses; ++i)
EXPECT(assert_equal(expected.at<Pose2>(i), actualChol.at<Pose2>(i), tol));
Values actualQR = isamQR.estimate();
for (size_t i=0; i<nrPoses; ++i)
EXPECT(assert_equal(expected.at<Pose2>(i), actualQR.at<Pose2>(i), tol));
// Check landmarks
EXPECT(assert_equal(expected.at<Point2>(lm1), actualChol.at<Point2>(lm1), tol));
EXPECT(assert_equal(expected.at<Point2>(lm2), actualChol.at<Point2>(lm2), tol));
EXPECT(assert_equal(expected.at<Point2>(lm3), actualChol.at<Point2>(lm3), tol));
EXPECT(assert_equal(expected.at<Point2>(lm1), actualQR.at<Point2>(lm1), tol));
EXPECT(assert_equal(expected.at<Point2>(lm2), actualQR.at<Point2>(lm2), tol));
EXPECT(assert_equal(expected.at<Point2>(lm3), actualQR.at<Point2>(lm3), tol));
}
/* ************************************************************************* */
TEST(testNonlinearISAM, markov_chain_with_reconnect ) {
int reorder_interval = 2;
NonlinearISAM isamChol(reorder_interval, EliminatePreferCholesky); // create an ISAM object
NonlinearISAM isamQR(reorder_interval, EliminateQR); // create an ISAM object
SharedDiagonal model3 = noiseModel::Diagonal::Sigmas(Vector3(3.0, 3.0, 0.5));
SharedDiagonal model2 = noiseModel::Diagonal::Sigmas(Vector2(2.0, 2.0));
Sampler sampler(model3, 42u);
// create initial graph
Pose2 cur_pose; // start at origin
NonlinearFactorGraph start_factors;
start_factors += NonlinearEquality<Pose2>(0, cur_pose);
Values init;
Values expected;
init.insert(0, cur_pose);
expected.insert(0, cur_pose);
isamChol.update(start_factors, init);
isamQR.update(start_factors, init);
size_t nrPoses = 21;
// create a constrained constellation of landmarks
Key lm1 = nrPoses+1, lm2 = nrPoses+2, lm3 = nrPoses+3;
Point2 landmark1(3., 4.), landmark2(6., 4.), landmark3(6., 9.);
expected.insert(lm1, landmark1);
expected.insert(lm2, landmark2);
expected.insert(lm3, landmark3);
// loop for a period of time to verify memory usage
Pose2 z(1.0, 2.0, 0.1);
for (size_t i=1; i<=nrPoses; ++i) {
NonlinearFactorGraph new_factors;
new_factors += BetweenFactor<Pose2>(i-1, i, z, model3);
Values new_init;
cur_pose = cur_pose.compose(z);
new_init.insert(i, cur_pose.retract(sampler.sample()));
expected.insert(i, cur_pose);
// Add a floating landmark constellation
if (i == 7) {
new_factors.addPrior(lm1, landmark1, model2);
new_factors.addPrior(lm2, landmark2, model2);
new_factors.addPrior(lm3, landmark3, model2);
// Initialize to origin
new_init.insert(lm1, Point2(0,0));
new_init.insert(lm2, Point2(0,0));
new_init.insert(lm3, Point2(0,0));
}
// Reconnect with observation later
if (i == 15) {
new_factors += BearingRangeFactor<Pose2, Point2>(
i, lm1, cur_pose.bearing(landmark1), cur_pose.range(landmark1), model2);
}
isamChol.update(new_factors, new_init);
isamQR.update(new_factors, new_init);
}
// verify values - all but the last one should be very close
Values actualChol = isamChol.estimate();
for (size_t i=0; i<nrPoses; ++i)
EXPECT(assert_equal(expected.at<Pose2>(i), actualChol.at<Pose2>(i), 1e-4));
Values actualQR = isamQR.estimate();
for (size_t i=0; i<nrPoses; ++i)
EXPECT(assert_equal(expected.at<Pose2>(i), actualQR.at<Pose2>(i), 1e-4));
// Check landmarks
EXPECT(assert_equal(expected.at<Point2>(lm1), actualChol.at<Point2>(lm1), tol));
EXPECT(assert_equal(expected.at<Point2>(lm2), actualChol.at<Point2>(lm2), tol));
EXPECT(assert_equal(expected.at<Point2>(lm3), actualChol.at<Point2>(lm3), tol));
EXPECT(assert_equal(expected.at<Point2>(lm1), actualQR.at<Point2>(lm1), tol));
EXPECT(assert_equal(expected.at<Point2>(lm2), actualQR.at<Point2>(lm2), tol));
EXPECT(assert_equal(expected.at<Point2>(lm3), actualQR.at<Point2>(lm3), tol));
}
/* ************************************************************************* */
TEST(testNonlinearISAM, loop_closures ) {
int relinearizeInterval = 100;
NonlinearISAM isam(relinearizeInterval);
// Create a Factor Graph and Values to hold the new data
NonlinearFactorGraph graph;
Values initialEstimate;
vector<string> lines;
lines.emplace_back("VERTEX2 0 0.000000 0.000000 0.000000");
lines.emplace_back("EDGE2 1 0 1.030390 0.011350 -0.012958");
lines.emplace_back("VERTEX2 1 1.030390 0.011350 -0.012958");
lines.emplace_back("EDGE2 2 1 1.013900 -0.058639 -0.013225");
lines.emplace_back("VERTEX2 2 2.043445 -0.060422 -0.026183");
lines.emplace_back("EDGE2 3 2 1.027650 -0.007456 0.004833");
lines.emplace_back("VERTEX2 3 3.070548 -0.094779 -0.021350");
lines.emplace_back("EDGE2 4 3 -0.012016 1.004360 1.566790");
lines.emplace_back("VERTEX2 4 3.079976 0.909609 1.545440");
lines.emplace_back("EDGE2 5 4 1.016030 0.014565 -0.016304");
lines.emplace_back("VERTEX2 5 3.091176 1.925681 1.529136");
lines.emplace_back("EDGE2 6 5 1.023890 0.006808 0.010981");
lines.emplace_back("VERTEX2 6 3.127018 2.948966 1.540117");
lines.emplace_back("EDGE2 7 6 0.957734 0.003159 0.010901");
lines.emplace_back("VERTEX2 7 3.153237 3.906347 1.551018");
lines.emplace_back("EDGE2 8 7 -1.023820 -0.013668 -3.093240");
lines.emplace_back("VERTEX2 8 3.146655 2.882457 -1.542222");
lines.emplace_back("EDGE2 9 8 1.023440 0.013984 -0.007802");
lines.emplace_back("EDGE2 9 5 0.033943 0.032439 -3.127400");
lines.emplace_back("VERTEX2 9 3.189873 1.859834 -1.550024");
lines.emplace_back("EDGE2 10 9 1.003350 0.022250 0.023491");
lines.emplace_back("EDGE2 10 3 0.044020 0.988477 -1.563530");
lines.emplace_back("VERTEX2 10 3.232959 0.857162 -1.526533");
lines.emplace_back("EDGE2 11 10 0.977245 0.019042 -0.028623");
lines.emplace_back("VERTEX2 11 3.295225 -0.118283 -1.555156");
lines.emplace_back("EDGE2 12 11 -0.996880 -0.025512 -3.126915");
lines.emplace_back("VERTEX2 12 3.254125 0.878076 1.601114");
lines.emplace_back("EDGE2 13 12 0.990646 0.018396 -0.016519");
lines.emplace_back("VERTEX2 13 3.205708 1.867709 1.584594");
lines.emplace_back("EDGE2 14 13 0.945873 0.008893 -0.002602");
lines.emplace_back("EDGE2 14 8 0.015808 0.021059 3.128310");
lines.emplace_back("VERTEX2 14 3.183765 2.813370 1.581993");
lines.emplace_back("EDGE2 15 14 1.000010 0.006428 0.028234");
lines.emplace_back("EDGE2 15 7 -0.014728 -0.001595 -0.019579");
lines.emplace_back("VERTEX2 15 3.166141 3.813245 1.610227");
auto model = noiseModel::Diagonal::Sigmas(Vector3(3.0, 3.0, 0.5));
// Loop over the different poses, adding the observations to iSAM incrementally
for (const string& str : lines) {
// scan the tag
string tag;
istringstream is(str);
if (!(is >> tag))
break;
// Check if vertex
const auto indexedPose = parseVertexPose(is, tag);
if (indexedPose) {
Key id = indexedPose->first;
initialEstimate.insert(Symbol('x', id), indexedPose->second);
if (id == 0) {
noiseModel::Diagonal::shared_ptr priorNoise =
noiseModel::Diagonal::Sigmas(Vector3(0.001, 0.001, 0.001));
graph.addPrior(Symbol('x', id), Pose2(0, 0, 0), priorNoise);
} else {
isam.update(graph, initialEstimate);
// Clear the factor graph and values for the next iteration
graph.resize(0);
initialEstimate.clear();
}
}
// check if edge
const auto betweenPose = parseEdge(is, tag);
if (betweenPose) {
size_t id1, id2;
tie(id1, id2) = betweenPose->first;
graph.emplace_shared<BetweenFactor<Pose2> >(Symbol('x', id2),
Symbol('x', id1), betweenPose->second, model);
}
}
EXPECT_LONGS_EQUAL(16, isam.estimate().size())
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
/* ************************************************************************* */
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