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GlobalMapNet: An Online Framework for Vectorized Global HD Map Construction
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GlobalMapNet: An Online Framework for Vectorized Global HD Map Construction
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High-definition (HD) maps are essential for autonomous driving systems. Traditionally, an expensive and labor-intensive pipeline is implemented to construct HD maps, which is limited in scalability. In recent years, crowdsourcing and online mapping have emerged as two alternative methods, but they have limitations respectively. In this paper, we provide a novel methodology, namely global map construction, to perform direct generation of vectorized global maps, combining the benefits of crowdsourcing and online mapping. We introduce GlobalMapNet, the first online framework for vectorized global HD map construction, which updates and utilizes a global map on the ego vehicle. To generate the global map from scratch, we propose GlobalMapBuilder to match and merge local maps continuously. We design a new algorithm, Map NMS, to remove duplicate map elements and produce a clean map. We also propose GlobalMapFusion to aggregate historical map information, improving consistency of prediction. We examine GlobalMapNet on two widely recognized datasets, Argoverse2 and nuScenes, showing that our framework is capable of generating globally consistent results.
Forward citations
Cited by 4 Pith papers
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The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
KITScenes Multimodal presents a new multimodal autonomous driving dataset with complete HD maps and four benchmarks for spatial learning tasks including online map construction and end-to-end driving.
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