122 lines
3.9 KiB
C++
122 lines
3.9 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 testSmartRangeFactor.cpp
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* @brief Unit tests for SmartRangeFactor Class
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* @author Frank Dellaert
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* @date Nov 2013
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*/
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#include <gtsam_unstable/slam/SmartRangeFactor.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <CppUnitLite/TestHarness.h>
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using namespace std;
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using namespace gtsam;
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static const double sigma = 2.0;
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/* ************************************************************************* */
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TEST( SmartRangeFactor, constructor ) {
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SmartRangeFactor f1;
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LONGS_EQUAL(0, f1.size())
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SmartRangeFactor f2(sigma);
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LONGS_EQUAL(0, f2.size())
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}
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/* ************************************************************************* */
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TEST( SmartRangeFactor, addRange ) {
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SmartRangeFactor f(sigma);
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f.addRange(1, 10);
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f.addRange(1, 12);
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LONGS_EQUAL(2, f.size())
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}
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/* ************************************************************************* */
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TEST( SmartRangeFactor, allAtOnce ) {
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// Test situation:
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Point2 p(0, 10);
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Pose2 pose1(0, 0, 0), pose2(5, 0, 0), pose3(5, 5, 0);
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double r1 = pose1.range(p), r2 = pose2.range(p), r3 = pose3.range(p);
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DOUBLES_EQUAL(10, r1, 1e-9);
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DOUBLES_EQUAL(sqrt(100+25), r2, 1e-9);
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DOUBLES_EQUAL(sqrt(50), r3, 1e-9);
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Values values; // all correct
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values.insert(1, pose1);
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values.insert(2, pose2);
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values.insert(3, pose3);
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SmartRangeFactor f(sigma);
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f.addRange(1, r1);
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// Whenever there are two ranges or less, error should be zero
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Vector actual1 = f.unwhitenedError(values);
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EXPECT(assert_equal(Vector_(1,0.0), actual1));
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f.addRange(2, r2);
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Vector actual2 = f.unwhitenedError(values);
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EXPECT(assert_equal(Vector_(1,0.0), actual2));
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f.addRange(3, r3);
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vector<Matrix> H(3);
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Vector actual3 = f.unwhitenedError(values);
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EXPECT_LONGS_EQUAL(3, f.keys().size());
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EXPECT(assert_equal(Vector_(1,0.0), actual3));
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// Check keys and Jacobian
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Vector actual4 = f.unwhitenedError(values, H); // with H now !
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EXPECT(assert_equal(Vector_(1,0.0), actual4));
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CHECK(assert_equal(Matrix_(1,3, 0.0,-1.0,0.0), H.front()));
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CHECK(assert_equal(Matrix_(1,3, sqrt(2)/2,-sqrt(2)/2,0.0), H.back()));
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// Test clone
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NonlinearFactor::shared_ptr clone = f.clone();
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EXPECT_LONGS_EQUAL(3, clone->keys().size());
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// Create initial value for optimization
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Values initial;
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initial.insert(1, Pose2(0, 0, 0));
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initial.insert(2, Pose2(5, 0, 0));
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initial.insert(3, Pose2(5, 6, 0));
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Vector actual5 = f.unwhitenedError(initial);
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EXPECT(assert_equal(Vector_(1,sqrt(25+16)-sqrt(50)), actual5));
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// Try optimizing
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NonlinearFactorGraph graph;
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graph.add(f);
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const noiseModel::Base::shared_ptr //
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priorNoise = noiseModel::Diagonal::Sigmas(Vector3(1, 1, M_PI));
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graph.add(PriorFactor<Pose2>(1, pose1, priorNoise));
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graph.add(PriorFactor<Pose2>(2, pose2, priorNoise));
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LevenbergMarquardtParams params;
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// params.setVerbosity("ERROR");
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Values result =
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LevenbergMarquardtOptimizer(graph, initial, params).optimize();
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EXPECT(assert_equal(values.at<Pose2>(1), result.at<Pose2>(1)));
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EXPECT(assert_equal(values.at<Pose2>(2), result.at<Pose2>(2)));
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// only the third pose will be changed, converges on following:
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EXPECT(assert_equal(Pose2(5.52159, 5.582727, 0), result.at<Pose2>(3),1e-5));
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}
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/* ************************************************************************* */
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int main() {
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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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