Divide and Conquer: A Systematic Approach for Industrial Scale High-Definition OpenDRIVE Generation from Sparse Point Clouds
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909269688844288 |
|---|---|
| author | Eisemann, Leon Maucher, Johannes |
| author_facet | Eisemann, Leon Maucher, Johannes |
| contents | High-definition road maps play a crucial role in the functionality and verification of highly automated driving functions. These contain precise information about the road network, geometry, condition, as well as traffic signs. Despite their importance for the development and evaluation of driving functions, the generation of high-definition maps is still an ongoing research topic. While previous work in this area has primarily focused on the accuracy of road geometry, we present a novel approach for automated large-scale map generation for use in industrial applications. Our proposed method leverages a minimal number of external information about the road to process LiDAR data in segments. These segments are subsequently combined, enabling a flexible and scalable process that achieves high-definition accuracy. Additionally, we showcase the use of the resulting OpenDRIVE in driving function simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18703 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Divide and Conquer: A Systematic Approach for Industrial Scale High-Definition OpenDRIVE Generation from Sparse Point Clouds Eisemann, Leon Maucher, Johannes Robotics High-definition road maps play a crucial role in the functionality and verification of highly automated driving functions. These contain precise information about the road network, geometry, condition, as well as traffic signs. Despite their importance for the development and evaluation of driving functions, the generation of high-definition maps is still an ongoing research topic. While previous work in this area has primarily focused on the accuracy of road geometry, we present a novel approach for automated large-scale map generation for use in industrial applications. Our proposed method leverages a minimal number of external information about the road to process LiDAR data in segments. These segments are subsequently combined, enabling a flexible and scalable process that achieves high-definition accuracy. Additionally, we showcase the use of the resulting OpenDRIVE in driving function simulation. |
| title | Divide and Conquer: A Systematic Approach for Industrial Scale High-Definition OpenDRIVE Generation from Sparse Point Clouds |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.18703 |