def do_imports(self,
task_manager: arvet.batch_analysis.task_manager.TaskManager,
path_manager: arvet.config.path_manager.PathManager,
db_client: arvet.database.client.DatabaseClient):
"""
Import image sources for evaluation in this experiment
:param task_manager: The task manager, for creating import tasks
:param path_manager: The path manager, for resolving file system paths
:param db_client: The database client, for saving declared objects too small to need a task
:return:
"""
# --------- EuRoC DATASETS -----------
# import specific EuRoC datasets that we have reference results for
for name, path in [
('EuRoC MH_01_easy', os.path.join('datasets', 'EuRoC',
'MH_01_easy')),
('EuRoC MH_04_difficult',
os.path.join('datasets', 'EuRoC', 'MH_04_difficult')),
]:
self.import_dataset(
name=name,
module_name='arvet_slam.dataset.euroc.euroc_loader',
path=path,
task_manager=task_manager,
path_manager=path_manager)
# --------- SYSTEMS -----------
# ORBSLAM2 - Create 2 variants, with different procesing modes
vocab_path = os.path.join('systems', 'ORBSLAM2', 'ORBvoc.txt')
self.import_system(name='ORBSLAM2 monocular',
db_client=db_client,
system=orbslam2.ORBSLAM2(
vocabulary_file=vocab_path,
mode=orbslam2.SensorMode.MONOCULAR,
settings={
'ORBextractor': {
'nFeatures': 1000,
'scaleFactor': 1.2,
'nLevels': 8,
'iniThFAST': 20,
'minThFAST': 7
}
}))
self.import_system(name='ORBSLAM2 stereo',
db_client=db_client,
system=orbslam2.ORBSLAM2(
vocabulary_file=vocab_path,
mode=orbslam2.SensorMode.STEREO,
settings={
'ThDepth': 35,
'ORBextractor': {
'nFeatures': 1200,
'scaleFactor': 1.2,
'nLevels': 8,
'iniThFAST': 20,
'minThFAST': 7
}
}))
def do_imports(self, task_manager: arvet.batch_analysis.task_manager.TaskManager,
path_manager: arvet.config.path_manager.PathManager,
db_client: arvet.database.client.DatabaseClient):
"""
Import image sources for evaluation in this experiment
:param task_manager: The task manager, for creating import tasks
:param path_manager: The path manager, for resolving file system paths
:param db_client: The database client, for saving declared objects too small to need a task
:return:
"""
# --------- KITTI DATASETS -----------
# import specific kitti datasets that we have reference results for
for sequence_num in {0, 3}:
self.import_dataset(
name='KITTI {0:02}'.format(sequence_num),
module_name='arvet_slam.dataset.kitti.kitti_loader',
path=os.path.join('datasets', 'KITTI', 'dataset'),
additional_args={'sequence_number': sequence_num},
task_manager=task_manager,
path_manager=path_manager
)
# --------- SYSTEMS -----------
# ORBSLAM2 - Create 2 variants, with different procesing modes
vocab_path = os.path.join('systems', 'ORBSLAM2', 'ORBvoc.txt')
for sensor_mode in {orbslam2.SensorMode.STEREO, orbslam2.SensorMode.MONOCULAR}:
self.import_system(
name='ORBSLAM2 {mode}'.format(mode=sensor_mode.name.lower()),
db_client=db_client,
system=orbslam2.ORBSLAM2(
vocabulary_file=vocab_path,
mode=sensor_mode,
settings={
'ThDepth': 35,
'ORBextractor': {
'nFeatures': 2000,
'scaleFactor': 1.2,
'nLevels': 8,
'iniThFAST': 20,
'minThFAST': 7
}
}
)
)
def do_imports(self,
task_manager: arvet.batch_analysis.task_manager.TaskManager,
path_manager: arvet.config.path_manager.PathManager,
db_client: arvet.database.client.DatabaseClient):
"""
Import image sources for evaluation in this experiment
:param task_manager: The task manager, for creating import tasks
:param path_manager: The path manager, for resolving file system paths
:param db_client: The database client, for saving declared objects too small to need a task
:return:
"""
# --------- TUM DATASETS -----------
# Import TUM datasets without using the manager, it is unnecessary
for folder in [
'rgbd_dataset_freiburg1_xyz', 'rgbd_dataset_freiburg1_desk'
]:
self.import_dataset(
name="TUM {0}".format(folder),
module_name='arvet_slam.dataset.tum.tum_loader',
path=os.path.join('datasets', 'TUM', folder),
task_manager=task_manager,
path_manager=path_manager)
# --------- SYSTEMS -----------
# ORBSLAM2 - Create 2 variants, with different procesing modes
vocab_path = os.path.join('systems', 'ORBSLAM2', 'ORBvoc.txt')
for sensor_mode in {
orbslam2.SensorMode.RGBD, orbslam2.SensorMode.MONOCULAR
}:
self.import_system(
name='ORBSLAM2 {mode}'.format(mode=sensor_mode.name.lower()),
db_client=db_client,
system=orbslam2.ORBSLAM2(vocabulary_file=vocab_path,
mode=sensor_mode,
settings={
'ThDepth': 35,
'ORBextractor': {
'nFeatures': 1000,
'scaleFactor': 1.2,
'nLevels': 8,
'iniThFAST': 20,
'minThFAST': 7
}
}))
def do_imports(self,
task_manager: arvet.batch_analysis.task_manager.TaskManager,
path_manager: arvet.config.path_manager.PathManager,
db_client: arvet.database.client.DatabaseClient):
"""
Import image sources for evaluation in this experiment
:param task_manager: The task manager, for creating import tasks
:param path_manager: The path manager, for resolving file system paths
:param db_client: The database client, for saving declared objects too small to need a task
:return:
"""
# --------- REAL WORLD DATASETS -----------
# Import KITTI datasets
for sequence_num in range(11):
self.import_dataset(
name='KITTI {0:02}'.format(sequence_num),
module_name='arvet_slam.dataset.kitti.kitti_loader',
path=os.path.join('datasets', 'KITTI', 'dataset'),
additional_args={'sequence_number': sequence_num},
task_manager=task_manager,
path_manager=path_manager)
# Import EuRoC datasets
for name, path in [
('EuRoC MH_01_easy', os.path.join('datasets', 'EuRoC',
'MH_01_easy')),
('EuRoC MH_02_easy', os.path.join('datasets', 'EuRoC',
'MH_02_easy')),
('EuRoC MH_02_medium',
os.path.join('datasets', 'EuRoC', 'MH_03_medium')),
('EuRoC MH_04_difficult',
os.path.join('datasets', 'EuRoC', 'MH_04_difficult')),
('EuRoC MH_05_difficult',
os.path.join('datasets', 'EuRoC', 'MH_05_difficult')),
('EuRoC V1_01_easy', os.path.join('datasets', 'EuRoC',
'V1_01_easy')),
('EuRoC V1_02_medium',
os.path.join('datasets', 'EuRoC', 'V1_02_medium')),
('EuRoC V1_03_difficult',
os.path.join('datasets', 'EuRoC', 'V1_03_difficult')),
('EuRoC V2_01_easy', os.path.join('datasets', 'EuRoC',
'V2_01_easy')),
('EuRoC V2_02_medium',
os.path.join('datasets', 'EuRoC', 'V2_02_medium')),
('EuRoC V2_03_difficult',
os.path.join('datasets', 'EuRoC', 'V2_03_difficult'))
]:
self.import_dataset(
name=name,
module_name='arvet_slam.dataset.euroc.euroc_loader',
path=path,
task_manager=task_manager,
path_manager=path_manager)
# Import TUM datasets without using the manager, it is unnecessary
for folder in arvet_slam.dataset.tum.tum_manager.dataset_names:
self.import_dataset(
name="TUM {0}".format(folder),
module_name='arvet_slam.dataset.tum.tum_loader',
path=os.path.join('datasets', 'TUM', folder),
task_manager=task_manager,
path_manager=path_manager)
# --------- SYSTEMS -----------
self.import_system(name='LibVisO',
system=libviso2.LibVisOSystem(),
db_client=db_client)
# ORBSLAM2 - Create 3 variants, with different procesing modes
vocab_path = os.path.join('systems', 'ORBSLAM2', 'ORBvoc.txt')
for sensor_mode in {
orbslam2.SensorMode.STEREO, orbslam2.SensorMode.RGBD,
orbslam2.SensorMode.MONOCULAR
}:
self.import_system(
name='ORBSLAM2 {mode}'.format(mode=sensor_mode.name.lower()),
db_client=db_client,
system=orbslam2.ORBSLAM2(
vocabulary_file=vocab_path,
mode=sensor_mode,
settings={'ORBextractor': {
'nFeatures': 1500
}}))
def do_imports(self, task_manager: arvet.batch_analysis.task_manager.TaskManager,
path_manager: arvet.config.path_manager.PathManager,
db_client: arvet.database.client.DatabaseClient):
"""
Import image sources for evaluation in this experiment
:param task_manager: The task manager, for creating import tasks
:param path_manager: The path manager, for finding dataset and simulator paths
:param db_client: The database client, for saving declared objects too small to need a task
:return:
"""
# --------- SIMULATORS -----------
# Add simulators explicitly, they have different metadata, so we can't just search
for exe, world_name, environment_type, light_level, time_of_day in [
(
'simulators/CorridorWorld/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'CorridorWorld', imeta.EnvironmentType.OUTDOOR_LANDSCAPE, imeta.LightingLevel.WELL_LIT,
imeta.TimeOfDay.DAY
)
]:
if world_name not in self._simulators:
simulator_id = dh.add_unique(db_client.image_source_collection, uecv_sim.UnrealCVSimulator(
executable_path=exe,
world_name=world_name,
environment_type=environment_type,
light_level=light_level,
time_of_day=time_of_day
))
self._simulators[world_name] = simulator_id
self._set_property('simulators.{0}'.format(world_name), simulator_id)
# --------- TRAJECTORY GROUPS -----------
for name, path in [
('forwards', get_forwards_trajectory()),
('upwards', get_upward_trajectory()),
('left', get_left_trajectory()),
('line roll', get_line_roll_trajectory()),
('line pitch', get_line_pitch_trajectory()),
('line yaw', get_line_yaw_trajectory()),
]:
if name not in self._trajectory_groups:
# First, create the trajectory follow controller with the desired trajectory
controller = follow_cont.TrajectoryFollowController(
trajectory=path,
trajectory_source='custom {0}'.format(name),
sequence_type=sequence_type.ImageSequenceType.SEQUENTIAL)
controller_id = dh.add_unique(db_client.image_source_collection, controller)
# Then create a trajectory group for it
self._trajectory_groups[name] = TrajectoryGroup(
name=name, controller_id=controller_id,
simulators={'CorridorWorld': self._simulators['CorridorWorld']})
self._set_property('trajectory_groups.{0}'.format(name), self._trajectory_groups[name].serialize())
for group in self._trajectory_groups.values():
if group.do_imports(task_manager):
self._set_property('trajectory_groups.{0}'.format(group.name), group.serialize())
# --------- SYSTEMS -----------
if self._libviso_system is None:
self._libviso_system = dh.add_unique(db_client.system_collection, libviso2.LibVisOSystem())
self._set_property('libviso', self._libviso_system)
# ORBSLAM2 - Create orbslam systems in each sensor mode
for sensor_mode in {orbslam2.SensorMode.STEREO, orbslam2.SensorMode.RGBD, orbslam2.SensorMode.MONOCULAR}:
name = 'ORBSLAM2 {mode}'.format(mode=sensor_mode.name.lower()).replace('.', '-')
vocab_path = os.path.join('systems', 'ORBSLAM2', 'ORBvoc.txt')
is_valid_path = True
try:
path_manager.find_file(vocab_path)
except FileNotFoundError:
is_valid_path = False
if name not in self._orbslam_systems and is_valid_path:
orbslam_id = dh.add_unique(db_client.system_collection, orbslam2.ORBSLAM2(
vocabulary_file=vocab_path,
mode=sensor_mode,
settings={
'ORBextractor': {'nFeatures': 1500}
}
))
self._orbslam_systems[name] = orbslam_id
self._set_property('orbslam_systems.{}'.format(name), orbslam_id)
def do_imports(self,
task_manager: arvet.batch_analysis.task_manager.TaskManager,
path_manager: arvet.config.path_manager.PathManager,
db_client: arvet.database.client.DatabaseClient):
"""
Import image sources for evaluation in this experiment
:param task_manager: The task manager, for creating import tasks
:param path_manager: The path manager, for resolving file system paths
:param db_client: The database client, for saving declared objects too small to need a task
:return:
"""
# --------- SIMULATORS -----------
# Add simulators explicitly, they have different metadata, so we can't just search
for exe, world_name, environment_type, light_level, time_of_day in [
('simulators/AIUE_V01_001/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_001', imeta.EnvironmentType.INDOOR,
imeta.LightingLevel.WELL_LIT, imeta.TimeOfDay.DAY),
('simulators/AIUE_V01_002/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_002', imeta.EnvironmentType.INDOOR,
imeta.LightingLevel.WELL_LIT, imeta.TimeOfDay.DAY),
('simulators/AIUE_V01_003/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_003', imeta.EnvironmentType.INDOOR,
imeta.LightingLevel.WELL_LIT, imeta.TimeOfDay.DAY),
('simulators/AIUE_V01_004/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_004', imeta.EnvironmentType.INDOOR,
imeta.LightingLevel.WELL_LIT, imeta.TimeOfDay.DAY),
(
'simulators/AIUE_V01_005/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_005', imeta.EnvironmentType.INDOOR,
imeta.LightingLevel.WELL_LIT, imeta.TimeOfDay.DAY
# ), (
# 'simulators/AIUE_V02_001/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
# 'AIUE_V02_001', imeta.EnvironmentType.INDOOR, imeta.LightingLevel.WELL_LIT,
# imeta.TimeOfDay.DAY
)
]:
self.import_simulator(executable_path=exe,
world_name=world_name,
environment_type=environment_type,
light_level=light_level,
time_of_day=time_of_day,
db_client=db_client)
# --------- REAL WORLD DATASETS -----------
# Import EuRoC datasets with lists of trajectory start points for each simulator
for name, path, mappings in [
('EuRoC MH_01_easy', os.path.join('datasets', 'EuRoC',
'MH_01_easy'),
euroc_origins.get_MH_01_easy()),
('EuRoC MH_02_easy', os.path.join('datasets', 'EuRoC',
'MH_02_easy'),
euroc_origins.get_MH_02_easy()),
('EuRoC MH_03_medium',
os.path.join('datasets', 'EuRoC',
'MH_03_medium'), euroc_origins.get_MH_03_medium()),
('EuRoC MH_04_difficult',
os.path.join('datasets', 'EuRoC', 'MH_04_difficult'),
euroc_origins.get_MH_04_difficult()),
('EuRoC MH_05_difficult',
os.path.join('datasets', 'EuRoC', 'MH_05_difficult'),
euroc_origins.get_MH_05_difficult()),
('EuRoC V1_01_easy', os.path.join('datasets', 'EuRoC',
'V1_01_easy'),
euroc_origins.get_V1_01_easy()),
('EuRoC V1_02_medium',
os.path.join('datasets', 'EuRoC',
'V1_02_medium'), euroc_origins.get_V1_02_medium()),
('EuRoC V1_03_difficult',
os.path.join('datasets', 'EuRoC', 'V1_03_difficult'),
euroc_origins.get_V1_03_difficult()),
('EuRoC V2_01_easy', os.path.join('datasets', 'EuRoC',
'V2_01_easy'),
euroc_origins.get_V2_01_easy()),
('EuRoC V2_02_medium',
os.path.join('datasets', 'EuRoC',
'V2_02_medium'), euroc_origins.get_V2_02_medium()),
('EuRoC V2_03_difficult',
os.path.join('datasets', 'EuRoC', 'V2_03_difficult'),
euroc_origins.get_V2_03_difficult())
]:
self.import_dataset(
module_name='arvet_slam.dataset.euroc.euroc_loader',
path=path,
name=name,
mappings=mappings,
task_manager=task_manager,
path_manager=path_manager)
# Import TUM datasets with lists of trajectory start points for each simulator
for folder, mappings in [
('rgbd_dataset_freiburg1_360', tum_origins.get_frieburg1_360()),
('rgbd_dataset_frieburg1_rpy', tum_origins.get_frieburg1_rpy()),
('rgbd_dataset_frieburg1_xyz', tum_origins.get_frieburg1_xyz()),
('rgbd_dataset_frieburg2_desk', tum_origins.get_frieburg2_desk()),
('rgbd_dataset_frieburg2_rpy', tum_origins.get_frieburg2_rpy()),
('rgbd_dataset_frieburg2_xyz', tum_origins.get_frieburg2_xyz()),
('rgbd_dataset_frieburg3_structure_texture_far',
tum_origins.get_frieburg3_structure_texture_far()),
('rgbd_dataset_frieburg3_walking_xyz',
tum_origins.get_frieburg3_walking_xyz())
]:
self.import_dataset(
module_name='arvet_slam.dataset.tum.tum_loader',
path=os.path.join('datasets', 'TUM', folder),
name="TUM {0}".format(folder),
mappings=mappings,
task_manager=task_manager,
path_manager=path_manager)
# Import KITTI datasets
for sequence_num in range(11):
self.import_dataset(
module_name='arvet_slam.dataset.kitti.kitti_loader',
name='KITTI trajectory {}'.format(sequence_num),
path=os.path.join('datasets', 'KITTI', 'dataset'),
additional_args={'sequence_number': sequence_num},
mappings=kitti_origins.get_mapping(sequence_num),
task_manager=task_manager,
path_manager=path_manager)
# --------- SYSTEMS -----------
# LibVisO2
self.import_system(name='LibVisO',
system=libviso2.LibVisOSystem(),
db_client=db_client)
# ORBSLAM2 - Create 2 variants, stereo and mono
# These datasets don't have
vocab_path = os.path.join('systems', 'ORBSLAM2', 'ORBvoc.txt')
for sensor_mode in {
orbslam2.SensorMode.STEREO, orbslam2.SensorMode.MONOCULAR,
orbslam2.SensorMode.RGBD
}:
self.import_system(
name='ORBSLAM2 {mode}'.format(mode=sensor_mode.name.lower()),
system=orbslam2.ORBSLAM2(
vocabulary_file=vocab_path,
mode=sensor_mode,
settings={'ORBextractor': {
'nFeatures': 1500
}}),
db_client=db_client)
# --------- BENCHMARKS -----------
# Add benchmarks to calculate the errors on a per-estimate and per-frame basis
self.import_benchmark(
name='Estimate Errors',
benchmark=estimate_errors_benchmark.EstimateErrorsBenchmark(),
db_client=db_client)
self.import_benchmark(
name='Frame Errors',
benchmark=frame_errors_benchmark.FrameErrorsBenchmark(),
db_client=db_client)
# --------- TRAJECTORY GROUPS -----------
# Update the trajectory groups
# We call this at the end so that any new ones created by import datasets will be updated and saved.
for trajectory_group in self.trajectory_groups.values():
self.update_trajectory_group(trajectory_group, task_manager,
db_client)
def do_imports(self, task_manager: arvet.batch_analysis.task_manager.TaskManager,
path_manager: arvet.config.path_manager.PathManager,
db_client: arvet.database.client.DatabaseClient):
"""
Import image sources for evaluation in this experiment
:param task_manager: The task manager, for creating import tasks
:param path_manager: The path manager, for resolving file system paths
:param db_client: The database client, for saving declared objects too small to need a task
:return:
"""
# --------- SIMULATORS -----------
# Add simulators explicitly, they have different metadata, so we can't just search
for exe, world_name, environment_type, light_level, time_of_day in [
(
'simulators/AIUE_V01_001/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_001', imeta.EnvironmentType.INDOOR, imeta.LightingLevel.WELL_LIT,
imeta.TimeOfDay.DAY
), (
'simulators/AIUE_V01_002/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_002', imeta.EnvironmentType.INDOOR, imeta.LightingLevel.WELL_LIT,
imeta.TimeOfDay.DAY
), (
'simulators/AIUE_V01_003/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_003', imeta.EnvironmentType.INDOOR, imeta.LightingLevel.WELL_LIT,
imeta.TimeOfDay.DAY
), (
'simulators/AIUE_V01_004/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_004', imeta.EnvironmentType.INDOOR, imeta.LightingLevel.WELL_LIT,
imeta.TimeOfDay.DAY
), (
'simulators/AIUE_V01_005/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
'AIUE_V01_005', imeta.EnvironmentType.INDOOR, imeta.LightingLevel.WELL_LIT,
imeta.TimeOfDay.DAY
# ), (
# 'simulators/AIUE_V02_001/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
# 'AIUE_V02_001', imeta.EnvironmentType.INDOOR, imeta.LightingLevel.WELL_LIT,
# imeta.TimeOfDay.DAY
)
]:
self.import_simulator(
executable_path=exe,
world_name=world_name,
environment_type=environment_type,
light_level=light_level,
time_of_day=time_of_day,
db_client=db_client
)
# --------- REAL WORLD DATASETS -----------
# Import EuRoC datasets with lists of trajectory start points for each simulator
for name, path, mappings in [
('EuRoC MH_01_easy', os.path.join('datasets', 'EuRoC', 'MH_01_easy'), euroc_origins.get_MH_01_easy()),
('EuRoC MH_02_easy', os.path.join('datasets', 'EuRoC', 'MH_02_easy'), euroc_origins.get_MH_02_easy()),
('EuRoC MH_03_medium', os.path.join('datasets', 'EuRoC', 'MH_03_medium'),
euroc_origins.get_MH_03_medium()),
('EuRoC MH_04_difficult', os.path.join('datasets', 'EuRoC', 'MH_04_difficult'),
euroc_origins.get_MH_04_difficult()),
('EuRoC MH_05_difficult', os.path.join('datasets', 'EuRoC', 'MH_05_difficult'),
euroc_origins.get_MH_05_difficult()),
('EuRoC V1_01_easy', os.path.join('datasets', 'EuRoC', 'V1_01_easy'), euroc_origins.get_V1_01_easy()),
('EuRoC V1_02_medium', os.path.join('datasets', 'EuRoC', 'V1_02_medium'),
euroc_origins.get_V1_02_medium()),
('EuRoC V1_03_difficult', os.path.join('datasets', 'EuRoC', 'V1_03_difficult'),
euroc_origins.get_V1_03_difficult()),
('EuRoC V2_01_easy', os.path.join('datasets', 'EuRoC', 'V2_01_easy'), euroc_origins.get_V2_01_easy()),
('EuRoC V2_02_medium', os.path.join('datasets', 'EuRoC', 'V2_02_medium'),
euroc_origins.get_V2_02_medium()),
('EuRoC V2_03_difficult', os.path.join('datasets', 'EuRoC', 'V2_03_difficult'),
euroc_origins.get_V2_03_difficult())
]:
self.import_dataset(
module_name='arvet_slam.dataset.euroc.euroc_loader',
path=path,
name=name,
mappings=mappings,
task_manager=task_manager,
path_manager=path_manager
)
# Import TUM datasets with lists of trajectory start points for each simulator
for folder, mappings in [
('rgbd_dataset_freiburg1_360', tum_origins.get_frieburg1_360()),
('rgbd_dataset_freiburg1_rpy', tum_origins.get_frieburg1_rpy()),
('rgbd_dataset_freiburg1_xyz', tum_origins.get_frieburg1_xyz()),
('rgbd_dataset_freiburg2_desk', tum_origins.get_frieburg2_desk()),
('rgbd_dataset_freiburg2_rpy', tum_origins.get_frieburg2_rpy()),
('rgbd_dataset_freiburg2_xyz', tum_origins.get_frieburg2_xyz()),
('rgbd_dataset_freiburg3_structure_texture_far', tum_origins.get_frieburg3_structure_texture_far()),
('rgbd_dataset_freiburg3_walking_xyz', tum_origins.get_frieburg3_walking_xyz())
]:
self.import_dataset(
module_name='arvet_slam.dataset.tum.tum_loader',
path=os.path.join('datasets', 'TUM', folder),
name="TUM {0}".format(folder),
mappings=mappings,
task_manager=task_manager,
path_manager=path_manager
)
# --------- SYSTEMS -----------
# LibVisO2
self.import_system(
name='LibVisO',
system=libviso2.LibVisOSystem(),
db_client=db_client
)
# ORBSLAM2 - Create 3 variants; stereo, mono, and rgbd
# These datasets don't have
vocab_path = os.path.join('systems', 'ORBSLAM2', 'ORBvoc.txt')
for sensor_mode in {orbslam2.SensorMode.STEREO, orbslam2.SensorMode.MONOCULAR, orbslam2.SensorMode.RGBD}:
self.import_system(
name='ORBSLAM2 {mode}'.format(mode=sensor_mode.name.lower()),
system=orbslam2.ORBSLAM2(
vocabulary_file=vocab_path,
mode=sensor_mode,
settings={'ORBextractor': {'nFeatures': 1500}}
),
db_client=db_client
)
# Do the superclass imports
super().do_imports(task_manager, path_manager, db_client)
def do_imports(self,
task_manager: arvet.batch_analysis.task_manager.TaskManager,
path_manager: arvet.config.path_manager.PathManager,
db_client: arvet.database.client.DatabaseClient):
"""
Import image sources for evaluation in this experiment
:param task_manager: The task manager, for creating import tasks
:param path_manager: The path manager, for resolving file system paths
:param db_client: The database client, for saving declared objects too small to need a task
:return:
"""
# --------- SIMULATORS -----------
# Add simulators explicitly, they have different metadata, so we can't just search
for exe, world_name, environment_type, light_level, time_of_day in [
# (
# 'simulators/AIUE_V01_001/LinuxNoEditor/tempTest/Binaries/Linux/tempTest',
# 'AIUE_V01_001', imeta.EnvironmentType.INDOOR, imeta.LightingLevel.WELL_LIT,
# imeta.TimeOfDay.DAY
# )
]:
self.import_simulator(executable_path=exe,
world_name=world_name,
environment_type=environment_type,
light_level=light_level,
time_of_day=time_of_day,
db_client=db_client)
# --------- REAL WORLD DATASETS -----------
# Import KITTI datasets
for sequence_num in range(11):
self.import_dataset(
module_name='arvet_slam.dataset.kitti.kitti_loader',
name='KITTI trajectory {}'.format(sequence_num),
path=os.path.join('datasets', 'KITTI', 'dataset'),
additional_args={'sequence_number': sequence_num},
mappings=kitti_origins.get_mapping(sequence_num),
task_manager=task_manager,
path_manager=path_manager,
db_client=db_client,
)
# --------- SYSTEMS -----------
# LibVisO2
self.import_system(name='LibVisO',
system=libviso2.LibVisOSystem(),
db_client=db_client)
# ORBSLAM2 - Create 3 variants, with different procesing modes
vocab_path = os.path.join('systems', 'ORBSLAM2', 'ORBvoc.txt')
for sensor_mode in {
orbslam2.SensorMode.STEREO, orbslam2.SensorMode.RGBD,
orbslam2.SensorMode.MONOCULAR
}:
self.import_system(
name='ORBSLAM2 {mode}'.format(mode=sensor_mode.name.lower()),
system=orbslam2.ORBSLAM2(
vocabulary_file=vocab_path,
mode=sensor_mode,
settings={'ORBextractor': {
'nFeatures': 1500
}}),
db_client=db_client)
# --------- BENCHMARKS -----------
# Create and store the benchmarks for camera trajectories
# Just using the default settings for now
self.import_benchmark(name='Relative Pose Error',
db_client=db_client,
benchmark=rpe.BenchmarkRPE(max_pairs=10000,
fixed_delta=False,
delta=1.0,
delta_unit='s',
offset=0,
scale_=1))
self.import_benchmark(name='Absolute Trajectory Error',
db_client=db_client,
benchmark=ate.BenchmarkATE(offset=0,
max_difference=0.2,
scale=1))
self.import_benchmark(
name='Trajectory Drift',
db_client=db_client,
benchmark=traj_drift.BenchmarkTrajectoryDrift(
segment_lengths=[100, 200, 300, 400, 500, 600, 700, 800],
step_size=10))
self.import_benchmark(name='Tracking Statistics',
db_client=db_client,
benchmark=tracking_benchmark.TrackingBenchmark(
initializing_is_lost=True))