Indoor Navigation 2D Data Set

 
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The dataset consists of '''''17''''' 2D maps of indoor office and living environments generated from the [https://github.com/StanfordVL/GibsonEnv/blob/master/gibson/data/README.md#download Gibson Database for Habitat-sim], which contains 3D scans of real environments. The maps also have .yaml files for direct loading with the ROS package map_server. The dataset additionally contains start and goal coordinates for evaluating navigation tasks. Features of the dataset:
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The dataset consists of '''''17''''' 2D maps of indoor office and living environments generated from a subset of the [https://github.com/StanfordVL/GibsonEnv/blob/master/gibson/data/README.md#download Gibson Database], which contains 3D scans of real environments. The maps also have .yaml files for direct loading with the ROS package map_server. The dataset additionally contains start and goal coordinates for evaluating navigation tasks. Features of the dataset:
  
 
* 17 environments represented as images with a resolution of 1cm per pixel.
 
* 17 environments represented as images with a resolution of 1cm per pixel.
 
* 1700 short navigation tasks (100 per environment), with the distance between start and goal (0,4]m and at least one obstacle in between.
 
* 1700 short navigation tasks (100 per environment), with the distance between start and goal (0,4]m and at least one obstacle in between.
* 425 long navigation tasks (25 per environment), with with random distance between start and goal, with intermediate goals set at a distance of 1m.
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* 425 long navigation tasks (25 per environment), with random distances between start and goal, with intermediate goals set at a distance of 1m.
  
 
=== Citation ===
 
=== Citation ===

Latest revision as of 14:05, 30 July 2020

In2d all envs.png


The dataset consists of 17 2D maps of indoor office and living environments generated from a subset of the Gibson Database, which contains 3D scans of real environments. The maps also have .yaml files for direct loading with the ROS package map_server. The dataset additionally contains start and goal coordinates for evaluating navigation tasks. Features of the dataset:

  • 17 environments represented as images with a resolution of 1cm per pixel.
  • 1700 short navigation tasks (100 per environment), with the distance between start and goal (0,4]m and at least one obstacle in between.
  • 425 long navigation tasks (25 per environment), with random distances between start and goal, with intermediate goals set at a distance of 1m.

Citation

If you use this dataset please cite our paper:

@inproceedings{dobrevskiskocaj2020in2d,
  title={Adaptive Dynamic Window Approach},
  author={Dobrevski, Matej and Sko{\v{c}}aj, Danijel},
  booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020}
}

Additionally please cite the Gibson Database, from which our dataset was generated:

@inproceedings{xiazamirhe2018gibsonenv,
  title={Gibson {Env}: real-world perception for embodied agents},
  author={Xia, Fei and R. Zamir, Amir and He, Zhiyang and Sax, Alexander and Malik, Jitendra and Savarese, Silvio},
  booktitle={Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on},
  year={2018},
  organization={IEEE}
}

DOWNLOAD:

The dataset can be downloaded here.