A travelable area boundary dataset for visual navigation of field robots
Abstract Travelable area boundaries not only constrain the movement of field robots but also indicate alternative guiding routes for dynamic objects. Publicly available road boundary datasets have outlined boundaries by binary segmentation labels. However, hard post-processes have to be done to extr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04457-3 |
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| _version_ | 1832571997807181824 |
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| author | Kai Zhang Xia Yuan Jiachen Xu Kaiyang Wang Shiwei Wu Chunxia Zhao |
| author_facet | Kai Zhang Xia Yuan Jiachen Xu Kaiyang Wang Shiwei Wu Chunxia Zhao |
| author_sort | Kai Zhang |
| collection | DOAJ |
| description | Abstract Travelable area boundaries not only constrain the movement of field robots but also indicate alternative guiding routes for dynamic objects. Publicly available road boundary datasets have outlined boundaries by binary segmentation labels. However, hard post-processes have to be done to extract from detected boundaries further semantics including the shapes of the boundaries and guiding routes, which poses challenges to a real-time visual navigation system without detailed prior maps. In addition, boundary detectors suffer from insufficient data collected from complex roads with severe occlusion and of different shapes. In this paper, a travelable area boundary dataset is semi-automatically built. 82.05% of the data is collected from bends, crossroads, T-shape roads and other irregular roads. Novel guiding semantics labels, shape labels and scene complexity labels are assigned to boundaries. With the support of the new dataset, travelable area boundary detectors could be trained, evaluated and fairly compared. The dataset can also be used to train, evaluate or test detectors for the road boundary detection task. |
| format | Article |
| id | doaj-art-4e61a7cf9ad747449be21d9b4b3daa78 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-4e61a7cf9ad747449be21d9b4b3daa782025-02-02T12:08:11ZengNature PortfolioScientific Data2052-44632025-01-0112111810.1038/s41597-025-04457-3A travelable area boundary dataset for visual navigation of field robotsKai Zhang0Xia Yuan1Jiachen Xu2Kaiyang Wang3Shiwei Wu4Chunxia Zhao5School of Computer Science and Engineering, Nanjing University of Science and TechnologySchool of Computer Science and Engineering, Nanjing University of Science and TechnologySoftware Development Department, Dahua TechnologySchool of Computer Science and Engineering, Nanjing University of Science and TechnologySchool of Computer Science and Engineering, Nanjing University of Science and TechnologySchool of Computer Science and Engineering, Nanjing University of Science and TechnologyAbstract Travelable area boundaries not only constrain the movement of field robots but also indicate alternative guiding routes for dynamic objects. Publicly available road boundary datasets have outlined boundaries by binary segmentation labels. However, hard post-processes have to be done to extract from detected boundaries further semantics including the shapes of the boundaries and guiding routes, which poses challenges to a real-time visual navigation system without detailed prior maps. In addition, boundary detectors suffer from insufficient data collected from complex roads with severe occlusion and of different shapes. In this paper, a travelable area boundary dataset is semi-automatically built. 82.05% of the data is collected from bends, crossroads, T-shape roads and other irregular roads. Novel guiding semantics labels, shape labels and scene complexity labels are assigned to boundaries. With the support of the new dataset, travelable area boundary detectors could be trained, evaluated and fairly compared. The dataset can also be used to train, evaluate or test detectors for the road boundary detection task.https://doi.org/10.1038/s41597-025-04457-3 |
| spellingShingle | Kai Zhang Xia Yuan Jiachen Xu Kaiyang Wang Shiwei Wu Chunxia Zhao A travelable area boundary dataset for visual navigation of field robots Scientific Data |
| title | A travelable area boundary dataset for visual navigation of field robots |
| title_full | A travelable area boundary dataset for visual navigation of field robots |
| title_fullStr | A travelable area boundary dataset for visual navigation of field robots |
| title_full_unstemmed | A travelable area boundary dataset for visual navigation of field robots |
| title_short | A travelable area boundary dataset for visual navigation of field robots |
| title_sort | travelable area boundary dataset for visual navigation of field robots |
| url | https://doi.org/10.1038/s41597-025-04457-3 |
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