ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD
Map Construction
- URL: http://arxiv.org/abs/2310.13378v2
- Date: Mon, 8 Jan 2024 03:26:40 GMT
- Title: ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD
Map Construction
- Authors: Jingyi Yu and Zizhao Zhang and Shengfu Xia and Jizhang Sang
- Abstract summary: We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors.
We exploit the properties of map elements to improve the performance of map construction.
- Score: 42.874195888422584
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a novel end-to-end pipeline for online long-range vectorized
high-definition (HD) map construction using on-board camera sensors. The
vectorized representation of HD maps, employing polylines and polygons to
represent map elements, is widely used by downstream tasks. However, previous
schemes designed with reference to dynamic object detection overlook the
structural constraints within linear map elements, resulting in performance
degradation in long-range scenarios. In this paper, we exploit the properties
of map elements to improve the performance of map construction. We extract more
accurate bird's eye view (BEV) features guided by their linear structure, and
then propose a hierarchical sparse map representation to further leverage the
scalability of vectorized map elements and design a progressive decoding
mechanism and a supervision strategy based on this representation. Our
approach, ScalableMap, demonstrates superior performance on the nuScenes
dataset, especially in long-range scenarios, surpassing previous
state-of-the-art model by 6.5 mAP while achieving 18.3 FPS. Code is available
at https://github.com/jingy1yu/ScalableMap.
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