136 lines
4.1 KiB
C++
136 lines
4.1 KiB
C++
/*
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* Copyright (c) 2014, 2015, 2016, Charles River Analytics, Inc.
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*/
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#include "robot_localization/ros_filter_types.h"
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#include <gtest/gtest.h>
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#include <limits>
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#include <vector>
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using RobotLocalization::Ekf;
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using RobotLocalization::RosEkf;
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using RobotLocalization::STATE_SIZE;
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class RosEkfPassThrough : public RosEkf
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{
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public:
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RosEkfPassThrough() : RosEkf(ros::NodeHandle(), ros::NodeHandle("~"))
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{
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}
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Ekf &getFilter()
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{
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return filter_;
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}
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};
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TEST(EkfTest, Measurements)
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{
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RosEkfPassThrough ekf;
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Eigen::MatrixXd initialCovar(15, 15);
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initialCovar.setIdentity();
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initialCovar *= 0.5;
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ekf.getFilter().setEstimateErrorCovariance(initialCovar);
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Eigen::VectorXd measurement(STATE_SIZE);
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measurement.setIdentity();
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for (size_t i = 0; i < STATE_SIZE; ++i)
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{
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measurement[i] = i * 0.01 * STATE_SIZE;
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}
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Eigen::MatrixXd measurementCovariance(STATE_SIZE, STATE_SIZE);
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measurementCovariance.setIdentity();
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for (size_t i = 0; i < STATE_SIZE; ++i)
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{
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measurementCovariance(i, i) = 1e-9;
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}
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std::vector<int> updateVector(STATE_SIZE, true);
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// Ensure that measurements are being placed in the queue correctly
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ros::Time time;
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time.fromSec(1000);
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ekf.enqueueMeasurement("odom0",
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measurement,
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measurementCovariance,
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updateVector,
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std::numeric_limits<double>::max(),
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time);
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ekf.integrateMeasurements(ros::Time(1001));
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EXPECT_EQ(ekf.getFilter().getState(), measurement);
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EXPECT_EQ(ekf.getFilter().getEstimateErrorCovariance(), measurementCovariance);
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ekf.getFilter().setEstimateErrorCovariance(initialCovar);
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// Now fuse another measurement and check the output.
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// We know what the filter's state should be when
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// this is complete, so we'll check the difference and
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// make sure it's suitably small.
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Eigen::VectorXd measurement2 = measurement;
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measurement2 *= 2.0;
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for (size_t i = 0; i < STATE_SIZE; ++i)
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{
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measurementCovariance(i, i) = 1e-9;
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}
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time.fromSec(1002);
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ekf.enqueueMeasurement("odom0",
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measurement2,
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measurementCovariance,
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updateVector,
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std::numeric_limits<double>::max(),
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time);
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ekf.integrateMeasurements(ros::Time(1003));
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measurement = measurement2.eval() - ekf.getFilter().getState();
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for (size_t i = 0; i < STATE_SIZE; ++i)
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{
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EXPECT_LT(::fabs(measurement[i]), 0.001);
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}
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}
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int main(int argc, char **argv)
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{
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ros::init(argc, argv, "ekf");
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testing::InitGoogleTest(&argc, argv);
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return RUN_ALL_TESTS();
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}
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