Alignment
function estimate_alignment
estimate_alignment(
traj_from: trajectopy.core.trajectory.Trajectory,
traj_to: trajectopy.core.trajectory.Trajectory,
alignment_settings: trajectopy.core.settings.alignment.AlignmentSettings = AlignmentSettings(preprocessing=AlignmentPreprocessing(min_speed=0.0, time_start=0.0, time_end=0.0), estimation_settings=AlignmentEstimationSettings(trans_x=True, trans_y=True, trans_z=True, rot_x=True, rot_y=True, rot_z=True, scale=False, time_shift=False, use_x_speed=True, use_y_speed=True, use_z_speed=True, lever_x=False, lever_y=False, lever_z=False, sensor_rotation=False, auto_update=False), stochastics=AlignmentStochastics(std_xy_from=1.0, std_z_from=1.0, std_xy_to=1.0, std_z_to=1.0, std_roll_pitch=0.017453292519943295, std_yaw=0.017453292519943295, std_speed=1.0, error_probability=0.05, variance_estimation=False), metric_threshold=0.0001, time_threshold=0.0001),
matching_settings: trajectopy.core.settings.matching.MatchingSettings = MatchingSettings(method=<MatchingMethod.INTERPOLATION: 'interpolation'>, max_time_diff=0.01, max_distance=0.0, k_nearest=10)
) → AlignmentResult
Aligns two trajectories
Performs a - Helmert - Leverarm - Time shift
estimation depending on the configuration. After this, the estimated parameters are applied to the 'traj_from' trajectory.
Args:
- traj_from (Trajectory)
- traj_to (Trajectory)
- alignment_settings (AlignmentSettings, optional): Settings for the alignment process. Defaults to AlignmentSettings().
- matching_settings (MatchingSettings, optional): Settings for the matching process. Defaults to MatchingSettings().
Returns:
AlignmentResult: Result of the alignment process
class AlignmentResult
Class to store the result of an alignment.
Attributes:
name(str): Name of the result.position_parameters(AlignmentParameters): Estimated position alignment parameters.rotation_parameters(SensorRotationParameters): Estimated rotation alignment parameters.estimation_of(AlignmentEstimationSettings): Settings defining which parameters were estimated.converged(bool): Whether the estimation converged.
method AlignmentResult.__init__
__init__(
name: str = 'Alignment Result',
position_parameters: trajectopy.core.alignment.parameters.AlignmentParameters = <factory>,
rotation_parameters: trajectopy.core.alignment.parameters.SensorRotationParameters = <factory>,
estimation_of: trajectopy.core.settings.alignment.AlignmentEstimationSettings = <factory>,
converged: bool = True
) → None
classmethod AlignmentResult.from_file
Load the result from a file.
Args:
filename(str): Path to the file.
Returns:
AlignmentResult: The loaded result.
method AlignmentResult.to_file
Save the result to a file.
Args:
filename(str): Path to the file.
class AlignmentData
Class holding the observation data required for Alignment
Attributes:
traj_from(Trajectory): Source trajectorytraj_to(Trajectory): Target trajectoryalignment_settings(AlignmentSettings): Settings for the alignmentmatching_settings(MatchingSettings): Settings for the matching
method AlignmentData.__init__
__init__(
traj_from: trajectopy.core.trajectory.Trajectory,
traj_to: trajectopy.core.trajectory.Trajectory,
alignment_settings: trajectopy.core.settings.alignment.AlignmentSettings,
matching_settings: trajectopy.core.settings.matching.MatchingSettings
) → None
property AlignmentData.est_euler_x
property AlignmentData.est_euler_y
property AlignmentData.est_euler_z
property AlignmentData.est_obs_vector
property AlignmentData.est_rpy_from
property AlignmentData.est_speed
property AlignmentData.est_speed_x
property AlignmentData.est_speed_y
property AlignmentData.est_speed_z
property AlignmentData.est_x_from
property AlignmentData.est_x_to
property AlignmentData.est_xyz_from
property AlignmentData.est_xyz_to
property AlignmentData.est_y_from
property AlignmentData.est_y_to
property AlignmentData.est_z_from
property AlignmentData.est_z_to
property AlignmentData.euler_x
property AlignmentData.euler_y
property AlignmentData.euler_z
property AlignmentData.group_stds
Returns the mean standard deviation for each group
property AlignmentData.num_obs_per_epoch
Returns the number of observations per epoch depending on the enabled estimation modes.
property AlignmentData.number_of_epochs
property AlignmentData.obs_vector
property AlignmentData.res_vector
property AlignmentData.rpy_from
property AlignmentData.sigma_ll
property AlignmentData.speed
property AlignmentData.speed_x
property AlignmentData.speed_y
property AlignmentData.speed_z
property AlignmentData.tstamps
property AlignmentData.var_vector
property AlignmentData.x_from
property AlignmentData.x_to
property AlignmentData.xyz_from
property AlignmentData.xyz_to
property AlignmentData.y_from
property AlignmentData.y_to
property AlignmentData.z_from
property AlignmentData.z_to
handler AlignmentData.observation_groups
handler AlignmentData.rpy_to
handler AlignmentData.thresholds
handler AlignmentData.variance_groups
method AlignmentData.build_obs_vector
build_obs_vector(
xyz_from: numpy.ndarray,
xyz_to: numpy.ndarray,
rot_from: Optional[trajectopy.core.rotationset.RotationSet],
speed: Optional[numpy.ndarray]
) → ndarray
Creates the observation vector required for the alignment adjustment.
Args:
xyz_from(np.ndarray): Positions to be aligned.xyz_to(np.ndarray): Target Positions.rpy_from(np.ndarray): The roll, pitch, and yaw angles of the platform. Those angles should describe the rotation of the body-fixed coordinate system with respect to the inertial coordinate system.
Returns:
np.ndarray: The observation vector required for the alignment adjustment.
method AlignmentData.build_res_vector
method AlignmentData.build_var_vector
Sets up the variance vector
Its size depends on whether the leverarm should be estimated or not. In this case, not only the source and the target positions are relevant but also the platform orientations. Also, when estimating the time shift, the platform speed is also considered.
Returns:
np.ndarray: variance vector
method AlignmentData.get_est_obs_group
method AlignmentData.get_obs_group
method AlignmentData.get_res_group
method AlignmentData.get_var_group
method AlignmentData.get_variance_estimation_subset
Returns a subset of the alignment data for variance estimation
This method will return a subset of the alignment data that contains 'num_obs' observations. The subset is selected by finding the epoch with the highest standard deviation of the observations. The idea is that a higher variance in the observations will lead to a better estimation of the variances.
The motivation behind this is that the variance estimation is memory and time consuming. Therefore, a subset of the observations is used for the estimation.
Args:
num_obs(int, optional): Subet size. Defaults to 200.
Returns:
AlignmentData: Cropped alignment data
method AlignmentData.set_obs_group
method AlignmentData.set_res_group
method AlignmentData.set_var_group
method AlignmentData.setup
Prepare two trajectories for alignment.
This method will filter the trajectories by speed and resample both trajectories to the same sampling.