def test_get_positions(self):
# create built-in array of coordinates (lat-lon)
coordinates = np.array([(44.484372, 11.355899), (44.484171, 11.355872),
(44.483912, 11.355410),
(44.483966, 11.355011)])
coordinates = np.deg2rad(coordinates)
# altitude is zero
altitudes = np.zeros(4)
# create expected distance (values are taken from a website)
expected_distance = np.array([0, 22.45, 64.20, 83.67])
# call method to test
positions, _ = get_positions(coordinates.T, altitudes)
# calculate distance from (0,0) with norm
distance = np.linalg.norm(positions, axis=0)
# ground truth is calculated online with a website that use haversine distance, while get_position doesn't
# so limit equality to 2 decimal digit. it would be enough for small distances (gnss is 40hz)
np.testing.assert_array_almost_equal(expected_distance,
distance,
decimal=2)
import matplotlib.pyplot as plt
if __name__ == '__main__':
window_size = 20
path = '../tests/test_fixtures/parking.tsv'
# currently default format is unmodified fullinertial but other formats are / will be supported
times, coordinates, altitudes, gps_speed, heading, accelerations, angular_velocities = parse_input(path, [
InputType.UNMOD_FULLINERTIAL])
converts_measurement_units(accelerations, angular_velocities, gps_speed, coordinates)
# get positions from GNSS data
gnss_positions, headings = get_positions(coordinates, altitudes)
# reduce accelerations disturbance
times, accelerations = reduce_disturbance(times, accelerations, window_size)
# reduce angular velocities disturbance
_, angular_velocities = reduce_disturbance(times, angular_velocities, window_size)
# truncate other array to match length of acc, thetas, times array
gnss_positions = gnss_positions[:, round(window_size / 2):-round(window_size / 2)]
# with "final" times now get velocities and
real_velocities = get_velocities(times, gnss_positions)
# scalar speed from GNSS position (better than from dataset because avoids Kalmar filter)
real_speeds = np.linalg.norm(real_velocities, axis=0)
# get time windows where vehicle is stationary
stationary_times = get_stationary_times(gps_speed)
def get_trajectory_from_path(path):
"""
parse input file from path, clean data and integrate positions
:param path: string input file
:return: 3 numpy array: 3xn position, 1xn times, 4xn angular position as quaternions
"""
window_size = 20
# currently default format is unmodified fullinertial but other formats are / will be supported
times, coordinates, altitudes, gps_speed, heading, accelerations, angular_velocities = parse_input(
path, [InputType.UNMOD_FULLINERTIAL])
converts_measurement_units(accelerations, angular_velocities, gps_speed,
coordinates, heading)
# get positions from GNSS data
gnss_positions, headings_2 = get_positions(coordinates, altitudes)
# reduce accelerations disturbance
times, accelerations = reduce_disturbance(times, accelerations,
window_size)
# reduce angular velocities disturbance
_, angular_velocities = reduce_disturbance(times, angular_velocities,
window_size)
# truncate other array to match length of acc, thetas, times array
gnss_positions = gnss_positions[:,
round(window_size /
2):-round(window_size / 2)]
# with "final" times now get velocities and
real_velocities = get_velocities(times, gnss_positions)
# scalar speed from GNSS position (better than from dataset because avoids Kalmar filter)
real_speeds = np.linalg.norm(real_velocities, axis=0)
# get time windows where vehicle is stationary
stationary_times = get_stationary_times(gps_speed)
# clear gyroscope drift
angular_velocities = clear_gyro_drift(angular_velocities, stationary_times)
# set times start to 0
normalize_timestamp(times)
# correct z-axis alignment
accelerations, angular_velocities = correct_z_orientation(
accelerations, angular_velocities, stationary_times)
# remove g
accelerations[2] -= accelerations[
2, stationary_times[0][0]:stationary_times[0][-1]].mean()
# correct alignment in xy plane
#accelerations = correct_xy_orientation(accelerations, angular_velocities)
motion_time = get_first_motion_time(stationary_times, gnss_positions)
initial_angular_position = get_initial_angular_position(
gnss_positions, motion_time)
# convert to laboratory frame of reference
accelerations, angular_positions = rotate_accelerations(
times, accelerations, angular_velocities, heading,
initial_angular_position)
# rotate to align y to north, x to east
accelerations = align_to_world(gnss_positions, accelerations, motion_time)
# angular position doesn't need to be aligned to world if starting angular position is already aligned and following
# angular positions are calculated from that
initial_speed = np.array([[gps_speed[0]], [0], [0]])
# integrate acceleration with gss velocities correction
correct_velocities = cumulative_integrate(times,
accelerations,
initial_speed,
adjust_data=real_velocities,
adjust_frequency=1)
if sign_inversion_is_necessary(correct_velocities):
accelerations *= -1
correct_velocities *= -1
correct_position = cumulative_integrate(times,
correct_velocities,
adjust_data=gnss_positions,
adjust_frequency=1)
return correct_position, times, angular_positions