MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps Article Swipe
Hang Wu
,
Zhenghao Zhang
,
Siyuan Lin
,
Xiangru Mu
,
Qiang Zhao
,
Ming Yang
,
Tong Qin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.08561
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.08561
Robust localization is the cornerstone of autonomous driving, especially in challenging urban environments where GPS signals suffer from multipath errors. Traditional localization approaches rely on high-definition (HD) maps, which consist of precisely annotated landmarks. However, building HD map is expensive and challenging to scale up. Given these limitations, leveraging navigation maps has emerged as a promising low-cost alternative for localization. Current approaches based on navigation maps can achieve highly accurate localization, but their complex matching strategies lead to unacceptable inference latency that fails to meet the real-time demands. To address these limitations, we propose a novel transformer-based neural re-localization method. Inspired by image registration, our approach performs a coarse-to-fine neural feature registration between navigation map and visual bird's-eye view features. Our method significantly outperforms the current state-of-the-art OrienterNet on both the nuScenes and Argoverse datasets, which is nearly 10%/20% localization accuracy and 30/16 FPS improvement on single-view and surround-view input settings, separately. We highlight that our research presents an HD-map-free localization method for autonomous driving, offering cost-effective, reliable, and scalable performance in challenging driving environments.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.08561
- https://arxiv.org/pdf/2407.08561
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400611945
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- OpenAlex ID
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https://openalex.org/W4400611945Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.08561Digital Object Identifier
- Title
-
MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation MapsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-11Full publication date if available
- Authors
-
Hang Wu, Zhenghao Zhang, Siyuan Lin, Xiangru Mu, Qiang Zhao, Ming Yang, Tong QinList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.08561Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2407.08561Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2407.08561Direct OA link when available
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Artificial intelligence, Computer vision, Feature (linguistics), Computer science, Image registration, Cartography, Computer graphics (images), Geography, Image (mathematics), Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.environments | 12 |
| abstract_inverted_index.limitations, | 47, 91 |
| abstract_inverted_index.localization | 1, 21, 139, 160 |
| abstract_inverted_index.registration | 111 |
| abstract_inverted_index.unacceptable | 78 |
| abstract_inverted_index.environments. | 174 |
| abstract_inverted_index.localization, | 70 |
| abstract_inverted_index.localization. | 59 |
| abstract_inverted_index.registration, | 103 |
| abstract_inverted_index.significantly | 122 |
| abstract_inverted_index.surround-view | 148 |
| abstract_inverted_index.coarse-to-fine | 108 |
| abstract_inverted_index.cost-effective, | 166 |
| abstract_inverted_index.high-definition | 25 |
| abstract_inverted_index.re-localization | 98 |
| abstract_inverted_index.state-of-the-art | 126 |
| abstract_inverted_index.transformer-based | 96 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |