Datasets:
Modalities:
Image
Languages:
English
ArXiv:
Tags:
Visual Nagivation
Proxy Map
Waypoint
Reinforcement Learning
Contrastive Learning
Intuitive Robot Motion Intent Visualization
DOI:
License:
File size: 4,341 Bytes
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# LAVN Dataset
### Data Organization
After downloading and unzipping the zip files, please reorganize the files in the following tructure:
```
LAVN
|--src
|--makeData_virtual.py
|--makeData_real.py
...
|--Virtual
|--Gibson
|--traj_<SCENE_ID>
|--worker_graph.json
|--rgb_<FRAME_ID>.jpg
|--depth_<FRAME_ID>.jpg
|--traj_Ackermanville
|--worker_graph.json
|--rgb_00001.jpg
|--rgb_00002.jpg
...
|--depth_00001.jpg
|--depth_00002.jpg
...
...
|--Matterport
|--traj_<SCENE_ID>
|--worker_graph.json
|--rgb_<FRAME_ID>.jpg
|--depth_<FRAME_ID>.jpg
|--traj_00000-kfPV7w3FaU5
|--worker_graph.json
|--rgb_00001.jpg
|--rgb_00002.jpg
...
|--depth_00001.jpg
|--depth_00002.jpg
...
...
|--Real
|--Campus
|--worker_graph.json
|--traj_480p_<SCENE_ID>
|--rgb_<FRAME_ID>.jpg
|--traj_480p_scene00
|--rgb_00001.jpg
```
where the main landmark annotation scripts ```makeData_virtual.py``` and ```makeData_real.py``` are in folder (1) ```src```. (2) ```Virtual``` and (3) ```Real``` stores trajectories collecetd in the simulation and real world, respectively. In each trajectory's data is collected in the following format:
```
|--traj_<SCENE_ID>
|--worker_graph.json
|--rgb_<FRAME_ID>.jpg
|--depth_<FRAME_ID>.jpg
```
where ```<SCENE_ID>``` matches exactly the original one in [Gibson](https://github.com/StanfordVL/GibsonEnv/blob/master/gibson/data/README.md) and [Matterport](https://aihabitat.org/datasets/hm3d/) run by the photo-realistic simulator [Habitat](https://github.com/facebookresearch/habitat-sim). Images are saved in either ```.jpg``` or ```.png``` format. Note that ```rgb``` images are the main visual representation while ```depth``` is the auxiliary visual information captured only in the virtual environment.
```worker_graph.json``` stores the meta data in dictionary in Python saved in ```json``` file with the following format:
```
{"node<NODE_ID>":
{"img_path": "./human_click_dataset/traj_<SCENE_ID>/rgb_<FRAME_ID>.jpg",
"depth_path": "./human_click_dataset/traj_<SCENE_ID>/depth_<FRAME_ID>.png",
"location": [<LOC_X>, <LOC_Y>, <LOC_Z>],
"orientation": <ORIENT>,
"click_point": [<COOR_X>, <COOR_Y>],
"reason": ""},
...
"node0":
{"img_path": "./human_click_dataset/traj_00101-n8AnEznQQpv/rgb_00002.jpg",
"depth_path": "./human_click_dataset/traj_00101-n8AnEznQQpv/depth_00002.jpg",
"location": [0.7419548034667969, -2.079209327697754, -0.5635206699371338],
"orientation": 0.2617993967423121,
"click_point": [270, 214],
"reason": ""}
...
"edges":...
"goal_location": null,
"start_location": [<LOC_X>, <LOC_Y>, <LOC_Z>],
"landmarks": [[[<COOR_X>, <COOR_Y>], <FRAME_ID>], ...],
"actions": ["ACTION_NAME", "turn_right", "move_forward", "turn_right", ...]
"env_name": <SCENE_ID>
}
```
where ```[<LOC_X>, <LOC_Y>, <LOC_Z>]``` is the 3-axis location vector, ```<ORIENT>``` is the orientation only in simulation. ```[<COOR_X>, <COOR_Y>]``` are the image coordinates of landmarks. ```ACTION_NAME``` stores the action of the robot take from the current frame to the next frame.
### Long-Term Maintenance Plan
We will conduct a long-term maintenance plan to ensure the accessability and quality for future research:
**Data Standards**: Data formats will be checked regularly with scripts to validate data consistency.
**Data Cleaning**: Data in incorrect formats, missing data or contains invalid values will be removed.
**Scheduled Updates**: We set up montly schedule for data updates.
**Storage Solutions**: Zenodo with a DOI will be provided to as an public repository for online storage. A second copy will be stored in a private cloud server while a third copy will be stored in a local drive.
**Data Backup**: Once one of the copies in the aforementioned storage approach is detected inaccessible, it will be restored by one of the other two copies immediately.
**Documentation**: Our documentation will be updated regularly reflecting feedback from users.
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