Online Mapping for Autonomous Driving: Addressing Sensor Generalization and Dynamic Map Updates in Campus Environments
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914066826526720 |
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| author | Zhang, Zihan Ravichandran, Abhijit Korti, Pragnya Wang, Luobin Christensen, Henrik I. |
| author_facet | Zhang, Zihan Ravichandran, Abhijit Korti, Pragnya Wang, Luobin Christensen, Henrik I. |
| contents | High-definition (HD) maps are essential for autonomous driving, providing precise information such as road boundaries, lane dividers, and crosswalks to enable safe and accurate navigation. However, traditional HD map generation is labor-intensive, expensive, and difficult to maintain in dynamic environments. To overcome these challenges, we present a real-world deployment of an online mapping system on a campus golf cart platform equipped with dual front cameras and a LiDAR sensor. Our work tackles three core challenges: (1) labeling a 3D HD map for campus environment; (2) integrating and generalizing the SemVecMap model onboard; and (3) incrementally generating and updating the predicted HD map to capture environmental changes. By fine-tuning with campus-specific data, our pipeline produces accurate map predictions and supports continual updates, demonstrating its practical value in real-world autonomous driving scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Mapping for Autonomous Driving: Addressing Sensor Generalization and Dynamic Map Updates in Campus Environments Zhang, Zihan Ravichandran, Abhijit Korti, Pragnya Wang, Luobin Christensen, Henrik I. Robotics Computer Vision and Pattern Recognition High-definition (HD) maps are essential for autonomous driving, providing precise information such as road boundaries, lane dividers, and crosswalks to enable safe and accurate navigation. However, traditional HD map generation is labor-intensive, expensive, and difficult to maintain in dynamic environments. To overcome these challenges, we present a real-world deployment of an online mapping system on a campus golf cart platform equipped with dual front cameras and a LiDAR sensor. Our work tackles three core challenges: (1) labeling a 3D HD map for campus environment; (2) integrating and generalizing the SemVecMap model onboard; and (3) incrementally generating and updating the predicted HD map to capture environmental changes. By fine-tuning with campus-specific data, our pipeline produces accurate map predictions and supports continual updates, demonstrating its practical value in real-world autonomous driving scenarios. |
| title | Online Mapping for Autonomous Driving: Addressing Sensor Generalization and Dynamic Map Updates in Campus Environments |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.25542 |