Local Map Construction with SDMap: A Comprehensive Survey
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916543032459264 |
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| author | Li, Jiaqi Jia, Pingfan Chen, Jiaxing Liu, Jiaxi He, Lei Li, Keqiang |
| author_facet | Li, Jiaqi Jia, Pingfan Chen, Jiaxing Liu, Jiaxi He, Lei Li, Keqiang |
| contents | Local map construction is a vital component of intelligent driving perception, offering necessary reference for vehicle positioning and planning. Standard Definition map (SDMap), known for its low cost, accessibility, and versatility, has significant potential as prior information for local map perception. This paper mainly reviews the local map construction methods with SDMap, including definitions, general processing flow, and datasets. Besides, this paper analyzes multimodal data representation and fusion methods in SDMap-based local map construction. This paper also discusses key challenges and future directions, such as optimizing SDMap processing, enhancing spatial alignment with real-time data, and incorporating richer environmental information. At last, the review looks forward to future research focusing on enhancing road topology inference and multimodal data fusion to improve the robustness and scalability of local map perception. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_02415 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Local Map Construction with SDMap: A Comprehensive Survey Li, Jiaqi Jia, Pingfan Chen, Jiaxing Liu, Jiaxi He, Lei Li, Keqiang Computer Vision and Pattern Recognition Local map construction is a vital component of intelligent driving perception, offering necessary reference for vehicle positioning and planning. Standard Definition map (SDMap), known for its low cost, accessibility, and versatility, has significant potential as prior information for local map perception. This paper mainly reviews the local map construction methods with SDMap, including definitions, general processing flow, and datasets. Besides, this paper analyzes multimodal data representation and fusion methods in SDMap-based local map construction. This paper also discusses key challenges and future directions, such as optimizing SDMap processing, enhancing spatial alignment with real-time data, and incorporating richer environmental information. At last, the review looks forward to future research focusing on enhancing road topology inference and multimodal data fusion to improve the robustness and scalability of local map perception. |
| title | Local Map Construction with SDMap: A Comprehensive Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.02415 |