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config.yaml
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config.yaml
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CORE_LIB_PATHS:
# Core libs are automatically imported by using:
# import config
g2o: thirdparty/g2opy/lib
pangolin: thirdparty/pangolin
orb_features: thirdparty/orbslam2_features/lib
pyslam_utils: cpp/utils/lib
thirdparty: thirdparty # considering the folders in thirdparty as modules
utilities: utilities
depth_estimation: depth_estimation
local_features: local_features
loop_closing: loop_closing
slam: slam
viz: viz
io: io
dense: dense
LIB_PATHS:
# The following libs are explicitely imported on demand by using, for instance:
# import config \ config.cfg.set_lib('tfeat')
lightglue: thirdparty/LightGlue
xfeat: thirdparty/accelerated_features
superpoint: thirdparty/superpoint
hardnet: thirdparty/hardnet
tfeat: thirdparty/tfeat
geodesc: thirdparty/geodesc
sosnet: thirdparty/SOSNet/codes
l2net: thirdparty/l2net
l2net_keras: thirdparty/l2net_keras/src
logpolar: thirdparty/logpolar
d2net: thirdparty/d2net
delf: thirdparty/tensorflow_models/research/delf,thirdparty/tensorflow_models/research/slim,thirdparty/tensorflow_models/research/
contextdesc: thirdparty/contextdesc
lfnet: thirdparty/lfnet
r2d2: thirdparty/r2d2
keynet: thirdparty/keynet
disk: thirdparty/disk
torch-dimcheck: thirdparty/disk/submodules/torch-dimcheck
torch-localize: thirdparty/disk/submodules/torch-localize
unets: thirdparty/disk/submodules/unets
pydbow3: thirdparty/pydbow3/lib
pydbow2: thirdparty/pydbow2/lib
pyibow: thirdparty/pyibow/lib
pyobindex2: thirdparty/pyibow/lib
vpr: thirdparty/vpr, thirdparty/patch_netvlad
depth_pro: thirdparty/ml_depth_pro/src
depth_anything_v2: thirdparty/depth_anything_v2/metric_depth
raft_stereo: thirdparty/raft_stereo, thirdparty/raft_stereo/core
crestereo: thirdparty/crestereo
crestereo_pytorch: thirdparty/crestereo_pytorch
#crestereo_onnx: thirdparty/crestereo_onnx
DATASET:
# select your dataset (decomment only one of the following lines)
#type: EUROC_DATASET
#type: KITTI_DATASET
#type: TUM_DATASET
#type: REPLICA_DATASET
type: VIDEO_DATASET
#type: FOLDER_DATASET
#type: LIVE_DATASET # Not recommended for current development stage
KITTI_DATASET:
type: kitti
sensor_type: stereo # Here, 'sensor_type' can be 'mono' or 'stereo'
base_path: /home/luigi/Work/datasets/rgbd_datasets/kitti_color/dataset
#
# name: '06'
# settings: settings/KITTI04-12.yaml # do not forget to correctly set the corresponding camera settings file
#
name: '00'
settings: settings/KITTI00-02.yaml # do not forget to correctly set the corresponding camera settings file
#
is_color: True # do you have the color images for the kitti dataset? (image2 and image3 folders)
groundtruth_file: auto
TUM_DATASET:
type: tum
sensor_type: rgbd # Here, 'sensor_type' can be 'mono' or 'rgbd'
base_path: /home/luigi/Work/datasets/rgbd_datasets/tum
#
#name: rgbd_dataset_freiburg3_long_office_household
#settings: settings/TUM3.yaml # do not forget to correctly set the corresponding camera settings file
#
#name: rgbd_dataset_freiburg1_xyz
#settings: settings/TUM1.yaml # do not forget to correctly set the corresponding camera settings file
#
name: rgbd_dataset_freiburg1_room # do not use this for mono, there are some in-place rotations during exploratory phases
settings: settings/TUM1.yaml # do not forget to set the corresponding camera settings file
#
associations: associations.txt
groundtruth_file: auto
EUROC_DATASET:
type: euroc
sensor_type: stereo # Here, sensor_type can be 'mono' or 'stereo'
base_path: /home/luigi/Work/datasets/rgbd_datasets/euroc
#name: MH01
#name: MH02
#name: MH03
#name: V101
name: V102
#name: V202
#name: V203
# 'settings' will be used when sensor_type: : 'mono'
settings: settings/EuRoC_mono.yaml
# 'settings_stereo' will be used when sensor_type: 'stereo' (if available)
settings_stereo: settings/EuRoC_stereo.yaml
associations: associations.txt
groundtruth_file: auto
start_frame_id: 0
REPLICA_DATASET:
type: replica
sensor_type: rgbd # Here, 'sensor_type' can be 'mono' or 'rgbd'
base_path: /home/luigi/Work/datasets/rgbd_datasets/replica
name: 'room2'
settings: settings/REPLICA.yaml # do not forget to correctly set the corresponding camera settings file
groundtruth_file: auto
VIDEO_DATASET:
type: video
sensor_type: mono # Here, 'sensor_type' can be only 'mono'
#
#base_path: ./data/videos/kitti00
#settings: settings/KITTI00-02.yaml
#name: video.mp4
#
base_path: ./data/videos/kitti06
settings: settings/KITTI04-12.yaml
name: video_color.mp4
#
#base_path: ./data/videos/webcam
#settings: settings/WEBCAM.yaml
#name: video.mp4
#
groundtruth_file: groundtruth.txt
timestamps: times.txt # to be intended as the frame timestamps
FOLDER_DATASET:
type: folder
sensor_type: mono # Here, 'sensor_type' can be only 'mono'
base_path: /home/luigi/Work/rgbd_datasets2/kitti/dataset/sequences/00/image_0/
# 'name' is used for specifying a glob pattern, e.g. *png, *jpeg, etc...
name: '*png'
settings: settings/KITTI00-02.yaml
groundtruth_file: groundtruth.txt
fps: 20
SYSTEM_STATE:
# This section is used for saving and re-loading the system state: Map + Loop closing state
load_state: False # flag to enable SLAM state reloading (map state + loop closing state) and relocalization
folder_path: data/slam_state # folder path relative to root of this repository
SAVE_TRAJECTORY:
save_trajectory: True
format_type: kitti # supported formats: `tum`, `kitti`, `euroc`
filename: kitti_trajectory.txt
# DO NOT USE [LIVE_DATASET]! This section is here for future developments.
# At the present time (see the README file):
# - main_vo.py cannot be used with your webcam since it requires a grountruth for recovering a correct inter-frame scale (see the README file)
# - main_slam.py does NOT have REAL-TIME processing capabilities yet (even if it does NOT need grountruth data)
# If you want to use your webcam, please, record a video by using calibration/save_video.py and then use it as a VIDEO_DATASET.
LIVE_DATASET:
type: live
base_path:
name: /dev/video2
settings: settings/WEBCAM.yaml
groundtruth_file: auto