Survey on Datasets for Perception in Unstructured Outdoor Environments
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913334458056704 |
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| author | Mortimer, Peter Maehlisch, Mirko |
| author_facet | Mortimer, Peter Maehlisch, Mirko |
| contents | Perception is an essential component of pipelines in field robotics. In this survey, we quantitatively compare publicly available datasets available in unstructured outdoor environments. We focus on datasets for common perception tasks in field robotics. Our survey categorizes and compares available research datasets. This survey also reports on relevant dataset characteristics to help practitioners determine which dataset fits best for their own application. We believe more consideration should be taken in choosing compatible annotation policies across the datasets in unstructured outdoor environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18750 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Survey on Datasets for Perception in Unstructured Outdoor Environments Mortimer, Peter Maehlisch, Mirko Computer Vision and Pattern Recognition Robotics Perception is an essential component of pipelines in field robotics. In this survey, we quantitatively compare publicly available datasets available in unstructured outdoor environments. We focus on datasets for common perception tasks in field robotics. Our survey categorizes and compares available research datasets. This survey also reports on relevant dataset characteristics to help practitioners determine which dataset fits best for their own application. We believe more consideration should be taken in choosing compatible annotation policies across the datasets in unstructured outdoor environments. |
| title | Survey on Datasets for Perception in Unstructured Outdoor Environments |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2404.18750 |