Road Graph Generator: Mapping roads at construction sites from GPS data
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915369756655616 |
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| author | Michałowska, Katarzyna Holmestad, Helga Margrete Bodahl Riemer-Sørensen, Signe |
| author_facet | Michałowska, Katarzyna Holmestad, Helga Margrete Bodahl Riemer-Sørensen, Signe |
| contents | We propose a new method for inferring roads from GPS trajectories to map construction sites. This task presents a unique challenge due to the erratic and non-standard movement patterns of construction machinery, which significantly diverge from typical vehicular traffic on established roads. Our proposed method first identifies intersections in the road network that serve as critical decision points, and then connects them with edges to produce a graph, which can subsequently be used for planning and task-allocation. We demonstrate the approach by mapping roads at a real-life construction site in Norway. The method is validated on four increasingly complex segments of the map. In our tests, the method achieved perfect accuracy in detecting intersections and inferring roads in data with no or low noise, while its performance was reduced in areas with significant noise and consistently missing GPS updates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09919 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Road Graph Generator: Mapping roads at construction sites from GPS data Michałowska, Katarzyna Holmestad, Helga Margrete Bodahl Riemer-Sørensen, Signe Artificial Intelligence We propose a new method for inferring roads from GPS trajectories to map construction sites. This task presents a unique challenge due to the erratic and non-standard movement patterns of construction machinery, which significantly diverge from typical vehicular traffic on established roads. Our proposed method first identifies intersections in the road network that serve as critical decision points, and then connects them with edges to produce a graph, which can subsequently be used for planning and task-allocation. We demonstrate the approach by mapping roads at a real-life construction site in Norway. The method is validated on four increasingly complex segments of the map. In our tests, the method achieved perfect accuracy in detecting intersections and inferring roads in data with no or low noise, while its performance was reduced in areas with significant noise and consistently missing GPS updates. |
| title | Road Graph Generator: Mapping roads at construction sites from GPS data |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2402.09919 |