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ekf2_test: add inertial nav falling detection tests

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bresch 2022-07-28 14:23:07 +02:00 committed by Daniel Agar
parent 66ce1a002b
commit 375753eba8
2 changed files with 227 additions and 0 deletions
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1
src/modules/ekf2/test/CMakeLists.txt
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@ -36,6 +36,7 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}/..)
add_subdirectory(sensor_simulator)
add_subdirectory(test_helper)
px4_add_unit_gtest(SRC test_EKF_accelerometer.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
px4_add_unit_gtest(SRC test_EKF_airspeed.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
px4_add_unit_gtest(SRC test_EKF_basics.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
px4_add_unit_gtest(SRC test_EKF_externalVision.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)

226
src/modules/ekf2/test/test_EKF_accelerometer.cpp Normal file
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@ -0,0 +1,226 @@
/****************************************************************************
*
* Copyright (c) 2022 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in
* the documentation and/or other materials provided with the
* distribution.
* 3. Neither the name PX4 nor the names of its contributors may be
* used to endorse or promote products derived from this software
* without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
****************************************************************************/
/**
* Accelerometer failure handling (bias, clipping, ...)
*/
#include <gtest/gtest.h>
#include "EKF/ekf.h"
#include "sensor_simulator/sensor_simulator.h"
#include "sensor_simulator/ekf_wrapper.h"
#include "test_helper/reset_logging_checker.h"
class EkfAccelerometerTest : public ::testing::Test
{
public:
EkfAccelerometerTest(): ::testing::Test(),
_ekf{std::make_shared<Ekf>()},
_sensor_simulator(_ekf),
_ekf_wrapper(_ekf) {};
std::shared_ptr<Ekf> _ekf;
SensorSimulator _sensor_simulator;
EkfWrapper _ekf_wrapper;
// Setup the Ekf with synthetic measurements
void SetUp() override
{
// run briefly to init, then manually set in air and at rest (default for a real vehicle)
_ekf->init(0);
_sensor_simulator.runSeconds(0.1);
_ekf->set_in_air_status(false);
_ekf->set_vehicle_at_rest(true);
_sensor_simulator.runSeconds(7);
}
// Use this method to clean up any memory, network etc. after each test
void TearDown() override
{
}
void testBias(float bias, float duration, float tolerance);
};
void EkfAccelerometerTest::testBias(float bias, float duration, float tolerance)
{
_sensor_simulator._imu.setAccelData(Vector3f(0.f, 0.f, -CONSTANTS_ONE_G + bias));
_sensor_simulator.runSeconds(duration);
Vector3f estimated_bias = _ekf->getAccelBias();
EXPECT_TRUE(matrix::isEqual(estimated_bias, Vector3f(0.f, 0.f, bias),
tolerance)) << "bias = " << bias << ", estimated = " << estimated_bias(2);
}
TEST_F(EkfAccelerometerTest, biasEstimateZero)
{
testBias(0.f, 10, 0.f);
}
TEST_F(EkfAccelerometerTest, biasEstimatePositive)
{
// The estimate should track a slowly changing bias
const float biases[4] = {0.1f, 0.2f, 0.3f, 0.38f};
for (int i = 0; i < 4; i ++) {
testBias(biases[i], 10, 0.03f);
}
}
TEST_F(EkfAccelerometerTest, biasEstimateNegative)
{
const float biases[4] = {-0.14f, -0.24f, -0.31, -0.4f};
for (int i = 0; i < 4; i ++) {
testBias(biases[i], 10, 0.03f);
}
}
TEST_F(EkfAccelerometerTest, imuFallingDetectionBaroOnly)
{
// GIVEN: an accelerometer with a really large Z bias
const float bias = CONSTANTS_ONE_G;
_sensor_simulator._imu.setAccelData(Vector3f(0.f, 0.f, -CONSTANTS_ONE_G + bias));
_sensor_simulator.runSeconds(2);
// WHEN: there is only one source of vertical aiding
// THEN: the estimator cannot know which one is wrong
EXPECT_FALSE(_ekf->fault_status_flags().bad_acc_vertical);
}
TEST_F(EkfAccelerometerTest, imuFallingDetectionBaroGnssVel)
{
// GIVEN: Baro and GNSS velocity fusion
_sensor_simulator.startGps();
_ekf_wrapper.enableGpsFusion();
_sensor_simulator.runSeconds(15);
EXPECT_TRUE(_ekf_wrapper.isIntendingBaroHeightFusion());
EXPECT_TRUE(_ekf_wrapper.isIntendingGpsFusion());
// AND: an accelerometer with a really large Z bias
const float bias = CONSTANTS_ONE_G;
_sensor_simulator._imu.setAccelData(Vector3f(0.f, 0.f, -CONSTANTS_ONE_G + bias));
_sensor_simulator.runSeconds(2);
// THEN: the bad vertical acceleration is detected
EXPECT_TRUE(_ekf->fault_status_flags().bad_acc_vertical);
}
TEST_F(EkfAccelerometerTest, imuFallingDetectionGnssOnly)
{
// GIVEN: GNSS height and velocity fusion
_sensor_simulator.startGps();
_ekf_wrapper.enableGpsFusion();
_ekf_wrapper.enableGpsHeightFusion();
_ekf_wrapper.disableBaroHeightFusion();
_sensor_simulator.runSeconds(15);
EXPECT_FALSE(_ekf_wrapper.isIntendingBaroHeightFusion());
EXPECT_TRUE(_ekf_wrapper.isIntendingGpsFusion());
EXPECT_TRUE(_ekf_wrapper.isIntendingGpsHeightFusion());
// AND: an accelerometer with a really large Z bias
const float bias = CONSTANTS_ONE_G;
_sensor_simulator._imu.setAccelData(Vector3f(0.f, 0.f, -CONSTANTS_ONE_G + bias));
_sensor_simulator.runSeconds(2);
// THEN: the bad vertical acceleration is not detected because both sources are of the same type
EXPECT_FALSE(_ekf->fault_status_flags().bad_acc_vertical);
}
TEST_F(EkfAccelerometerTest, imuFallingDetectionBaroRange)
{
// GIVEN: baro and range height fusion
_sensor_simulator._rng.setData(1.f, 100);
_sensor_simulator._rng.setLimits(0.1f, 9.f);
_sensor_simulator.startRangeFinder();
_ekf_wrapper.enableRangeHeightFusion();
_sensor_simulator.runSeconds(5);
EXPECT_TRUE(_ekf_wrapper.isIntendingBaroHeightFusion());
EXPECT_TRUE(_ekf_wrapper.isIntendingRangeHeightFusion());
// AND: an accelerometer with a really large Z bias
const float bias = CONSTANTS_ONE_G;
_sensor_simulator._imu.setAccelData(Vector3f(0.f, 0.f, -CONSTANTS_ONE_G + bias));
_sensor_simulator.runSeconds(2);
// THEN: the bad vertical is detected because both sources agree
EXPECT_TRUE(_ekf->fault_status_flags().bad_acc_vertical);
}
TEST_F(EkfAccelerometerTest, imuFallingDetectionBaroEvVel)
{
// GIVEN: baro and EV vel fusion
_ekf_wrapper.enableExternalVisionVelocityFusion();
_sensor_simulator.startExternalVision();
_sensor_simulator.runSeconds(1);
EXPECT_TRUE(_ekf_wrapper.isIntendingBaroHeightFusion());
EXPECT_TRUE(_ekf_wrapper.isIntendingExternalVisionVelocityFusion());
EXPECT_FALSE(_ekf_wrapper.isIntendingExternalVisionPositionFusion());
// AND: an accelerometer with a really large Z bias
const float bias = CONSTANTS_ONE_G;
_sensor_simulator._imu.setAccelData(Vector3f(0.f, 0.f, -CONSTANTS_ONE_G + bias));
_sensor_simulator.runSeconds(2);
// THEN: the bad vertical is detected because both sources agree
EXPECT_TRUE(_ekf->fault_status_flags().bad_acc_vertical);
}
TEST_F(EkfAccelerometerTest, imuFallingDetectionEvVelHgt)
{
// GIVEN: EV height and vel fusion
_ekf_wrapper.enableExternalVisionVelocityFusion();
_ekf_wrapper.enableExternalVisionHeightFusion();
_sensor_simulator.startExternalVision();
_ekf_wrapper.disableBaroHeightFusion();
_sensor_simulator.runSeconds(1);
EXPECT_TRUE(_ekf_wrapper.isIntendingExternalVisionVelocityFusion());
EXPECT_TRUE(_ekf_wrapper.isIntendingExternalVisionHeightFusion());
EXPECT_FALSE(_ekf_wrapper.isIntendingExternalVisionPositionFusion());
EXPECT_FALSE(_ekf_wrapper.isIntendingBaroHeightFusion());
// AND: an accelerometer with a really large Z bias
const float bias = CONSTANTS_ONE_G;
_sensor_simulator._imu.setAccelData(Vector3f(0.f, 0.f, -CONSTANTS_ONE_G + bias));
_sensor_simulator.runSeconds(2);
// THEN: the bad vertical acceleration is not detected because both sources are of the same type
EXPECT_FALSE(_ekf->fault_status_flags().bad_acc_vertical);
}