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Field Robotics Workshop Challenge Information
This branch currently contains the scripts and tools to work with the GOOSE Dataset and run baseline experiments for the Field Robotics workshop challenge at ICRA 2025.
More information on how to participate can be found in the Codabench Challenge website (2D, 3D) and the image_processing and pointcloud_processing subfolders.
For the challenge, we use the simplified label set listed below. This version of the labels can be downloaded from here and used to replace the original ones.
| name | label_key | hex |
|---|---|---|
| other | 0 | #A9A9A9 |
| artificial_structures | 1 | #DE88DE |
| artificial_ground | 2 | #EBFF3B |
| natural_ground | 3 | #A1887F |
| obstacle | 4 | #FFC107 |
| vehicle | 5 | #F44336 |
| vegetation | 6 | #4CAF50 |
| human | 7 | #8FB0FF |
| sky | 8 | #2196F3 |
The data structure and more in-depth information about the format can be found int the documentation. The data is divided into 3 splits: train, test and validation. Labeled data is available for train and validation splits.
It can be downloaded from our webpage.
In scripts you can find some sample scripts to directly download and unpack the 2D data.
Under the folder common some general configuration files and utils such as color maps can be found.
For more specific tools regarding training and data handling, have a look at the image_processing and pointcloud_processing subfolders.
Please cite us if this data is useful for you work:
@article{goose-dataset,
author = {Peter Mortimer and Raphael Hagmanns and Miguel Granero
and Thorsten Luettel and Janko Petereit and Hans-Joachim Wuensche},
title = {The GOOSE Dataset for Perception in Unstructured Environments},
url={https://arxiv.org/abs/2310.16788},
conference={2024 IEEE International Conference on Robotics and Automation (ICRA)}
year = 2024
}
@article{goose-ex-dataset,
author = {Raphael Hagmanns and Peter Mortimer and Miguel Granero
and Thorsten Luettel and Janko Petereit},
title = {Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentation},
url={},
conference={TBA}
year = 2024
} - This repository is licensed under the MIT License.
- The data is published under the CC BY-SA 4.0 License.
GOOSE is a project of Fraunhofer IOSB, UniBW Munich and University of Koblenz.