def test_benchmark_results_estimates_no_error_for_noiseless_trajectory(
self):
# Create a new computed trajectory with no noise, but a fixed offset from the real trajectory
# That is, the relative motions are the same, but the start point is different
for trial_result in self.trial_results:
comp_traj, _ = create_noise(
trajectory=trial_result.ground_truth_trajectory,
random_state=self.random,
time_offset=0,
time_noise=0,
loc_noise=0,
rot_noise=0)
trial_result.computed_trajectory = comp_traj
benchmark = feb.FrameErrorsBenchmark()
result = benchmark.benchmark_results(self.trial_results)
# Check all the errors are zero
values = collect_values(result, 0)
self.assertNPClose(np.zeros(values.shape), values)
values = collect_values(result, 1)
self.assertNPClose(np.zeros(values.shape), values)
values = collect_values(result, 2)
self.assertNPClose(np.zeros(values.shape), values, atol=1e-7)
values = collect_values(result, 3)
self.assertNPClose(np.zeros(values.shape), values, atol=1e-7)
def test_benchmark_results_estimates_reasonable_trajectory_error_per_frame(
self):
benchmark = feb.FrameErrorsBenchmark()
result = benchmark.benchmark_results(self.trial_results)
# The noise added to each location is <10, converting to motions makes that <20, in each axis
# But then, changes to the orientation tweak the relative location, and hence the motion
self.assertLessEqual(np.max(collect_values(result, 0)), 100)
self.assertLessEqual(np.max(collect_values(result, 2)), np.pi / 32)
def test_benchmark_results_returns_a_benchmark_result(self):
benchmark = feb.FrameErrorsBenchmark()
result = benchmark.benchmark_results(self.trial_results)
self.assertIsInstance(result, arvet.core.benchmark.BenchmarkResult)
self.assertNotIsInstance(result, arvet.core.benchmark.FailedBenchmark)
self.assertEqual(benchmark.identifier, result.benchmark)
self.assertEqual(
set(trial_result.identifier
for trial_result in self.trial_results),
set(result.trial_results))
def test_benchmark_results_one_observation_per_frame(self):
benchmark = feb.FrameErrorsBenchmark()
result = benchmark.benchmark_results(self.trial_results)
if isinstance(result, arvet.core.benchmark.FailedBenchmark):
print(result.reason)
self.assertEqual(
len(self.trial_results[0].ground_truth_trajectory) - 1,
len(result.frame_errors))
for error_measurement in result.frame_errors.values():
self.assertEqual(18, len(error_measurement))
def test_benchmark_results_fails_for_trials_from_different_systems(self):
trajectory = create_random_trajectory(self.random)
mixed_trial_results = self.trial_results + [
MockTrialResult(gt_trajectory=trajectory,
comp_trajectory=trajectory,
system_id=bson.ObjectId())
]
# Perform the benchmark
benchmark = feb.FrameErrorsBenchmark()
result = benchmark.benchmark_results(mixed_trial_results)
self.assertIsInstance(result, arvet.core.benchmark.FailedBenchmark)
def test_benchmark_results_estimates_no_noise_for_identical_trajectory(
self):
# Make all the trial results have exactly the same computed trajectories
for trial_result in self.trial_results[1:]:
trial_result.computed_trajectory = copy.deepcopy(
self.trial_results[0].computed_trajectory)
benchmark = feb.FrameErrorsBenchmark()
result = benchmark.benchmark_results(self.trial_results)
# Check all the errors are zero
values = collect_values(result, 4)
self.assertNPClose(np.zeros(values.shape), values)
values = collect_values(result, 5)
self.assertNPClose(np.zeros(values.shape), values)
values = collect_values(result, 6)
self.assertNPClose(np.zeros(values.shape), values, atol=1e-7)
values = collect_values(result, 7)
self.assertNPClose(np.zeros(values.shape), values, atol=1e-7)
def test_benchmark_results_estimates_no_error_for_identical_trajectory(
self):
# Copy the ground truth exactly
for trial_result in self.trial_results:
trial_result.computed_trajectory = copy.deepcopy(
trial_result.ground_truth_trajectory)
benchmark = feb.FrameErrorsBenchmark()
result = benchmark.benchmark_results(self.trial_results)
if isinstance(result, arvet.core.benchmark.FailedBenchmark):
print(result.reason)
# Check all the errors are zero
values = collect_values(result, 0)
self.assertNPClose(np.zeros(values.shape), values)
values = collect_values(result, 1)
self.assertNPClose(np.zeros(values.shape), values)
# We need more tolerance for the rotational error, because of the way the arccos
# results in the smallest possible change producing a value around 2e-8
values = collect_values(result, 2)
self.assertNPClose(np.zeros(values.shape), values, atol=1e-7)
values = collect_values(result, 3)
self.assertNPClose(np.zeros(values.shape), values, atol=1e-7)
def test_benchmark_results_fails_for_no_observations(self):
# Adjust the computed timestamps so none of them match
for trial_result in self.trial_results:
trial_result.computed_trajectory = {
time + 10000: pose
for time, pose in trial_result.computed_trajectory.items()
}
trial_result.tracking_states = {
time + 10000: state
for time, state in trial_result.tracking_states.items()
}
trial_result.num_features = {
time + 10000: features
for time, features in trial_result.num_features.items()
}
trial_result.num_matches = {
time + 10000: features
for time, features in trial_result.num_matches.items()
}
# Perform the benchmark
benchmark = feb.FrameErrorsBenchmark()
result = benchmark.benchmark_results(self.trial_results)
self.assertIsInstance(result, arvet.core.benchmark.FailedBenchmark)
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 make_instance(self, *args, **kwargs):
return feb.FrameErrorsBenchmark(*args, **kwargs)
