def ape(traj_ref: PosePath3D, traj_est: PosePath3D,
pose_relation: metrics.PoseRelation, align: bool = False,
correct_scale: bool = False, n_to_align: int = -1,
align_origin: bool = False, ref_name: str = "reference",
est_name: str = "estimate") -> Result:
# Align the trajectories.
only_scale = correct_scale and not align
if align or correct_scale:
logger.debug(SEP)
traj_est.align(traj_ref, correct_scale, only_scale, n=n_to_align)
elif align_origin:
logger.debug(SEP)
traj_est.align_origin(traj_ref)
# Calculate APE.
logger.debug(SEP)
data = (traj_ref, traj_est)
ape_metric = metrics.APE(pose_relation)
ape_metric.process_data(data)
title = str(ape_metric)
if align and not correct_scale:
title += "\n(with SE(3) Umeyama alignment)"
elif align and correct_scale:
title += "\n(with Sim(3) Umeyama alignment)"
elif only_scale:
title += "\n(scale corrected)"
elif align_origin:
title += "\n(with origin alignment)"
else:
title += "\n(not aligned)"
if (align or correct_scale) and n_to_align != -1:
title += " (aligned poses: {})".format(n_to_align)
ape_result = ape_metric.get_result(ref_name, est_name)
ape_result.info["title"] = title
logger.debug(SEP)
logger.info(ape_result.pretty_str())
ape_result.add_trajectory(ref_name, traj_ref)
ape_result.add_trajectory(est_name, traj_est)
if isinstance(traj_est, PoseTrajectory3D):
seconds_from_start = [
t - traj_est.timestamps[0] for t in traj_est.timestamps
]
ape_result.add_np_array("seconds_from_start", seconds_from_start)
ape_result.add_np_array("timestamps", traj_est.timestamps)
return ape_result
def rpe(traj_ref: PosePath3D,
traj_est: PosePath3D,
pose_relation: metrics.PoseRelation,
delta: float,
delta_unit: metrics.Unit,
rel_delta_tol: float = 0.1,
all_pairs: bool = False,
align: bool = False,
correct_scale: bool = False,
n_to_align: int = -1,
align_origin: bool = False,
ref_name: str = "reference",
est_name: str = "estimate",
support_loop: bool = False) -> Result:
# Align the trajectories.
only_scale = correct_scale and not align
if align or correct_scale:
logger.debug(SEP)
traj_est.align(traj_ref, correct_scale, only_scale, n=n_to_align)
elif align_origin:
logger.debug(SEP)
traj_est.align_origin(traj_ref)
# Calculate RPE.
logger.debug(SEP)
data = (traj_ref, traj_est)
rpe_metric = metrics.RPE(pose_relation, delta, delta_unit, rel_delta_tol,
all_pairs)
rpe_metric.process_data(data)
title = str(rpe_metric)
if align and not correct_scale:
title += "\n(with SE(3) Umeyama alignment)"
elif align and correct_scale:
title += "\n(with Sim(3) Umeyama alignment)"
elif only_scale:
title += "\n(scale corrected)"
elif align_origin:
title += "\n(with origin alignment)"
else:
title += "\n(not aligned)"
if (align or correct_scale) and n_to_align != -1:
title += " (aligned poses: {})".format(n_to_align)
rpe_result = rpe_metric.get_result(ref_name, est_name)
rpe_result.info["title"] = title
logger.debug(SEP)
logger.info(rpe_result.pretty_str())
# Restrict trajectories to delta ids for further processing steps.
if support_loop:
# Avoid overwriting if called repeatedly e.g. in Jupyter notebook.
import copy
traj_ref = copy.deepcopy(traj_ref)
traj_est = copy.deepcopy(traj_est)
traj_ref.reduce_to_ids(rpe_metric.delta_ids)
traj_est.reduce_to_ids(rpe_metric.delta_ids)
rpe_result.add_trajectory(ref_name, traj_ref)
rpe_result.add_trajectory(est_name, traj_est)
if isinstance(traj_est, PoseTrajectory3D):
seconds_from_start = [
t - traj_est.timestamps[0] for t in traj_est.timestamps
]
rpe_result.add_np_array("seconds_from_start", seconds_from_start)
rpe_result.add_np_array("timestamps", traj_est.timestamps)
return rpe_result
