DOI: 10.5281/zenodo.10608067
title: Human Navigation Dataset

description: We propose a novel Human Navigation Dataset (HND) for map representation research. HND provides unique landmark information in specific scenes to aid model navigation and exploration performance and map representation learning. Our dataset facilitates topological map representation learning by injecting novel landmarks into a robot's observation.
description: HumanNav Dataset File Structure

The root folder ```HumanNav``` consists of two folders, including (1) ```src``` and (2) ```traj```:```HumanNav   |--README.md   |--src      |--makeData_virtual.py   |--traj      |--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            ...         ...```where the main landmark annotation script ```makeData_virtual.py``` stores trajectories collecetd in the simulation. 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.