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arXiv · 2503.03882

IC-Mapper: Instance-Centric Spatio-Temporal Modeling for Online Vectorized Map Construction

Jiangtong Zhu·Zhao Yang·Yinan Shi·Jianwu Fang·Jianru Xue
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Abstract

Online vector map construction based on visual data can bypass the processes of data collection, post-processing, and manual annotation required by traditional map construction, which significantly enhances map-building efficiency. However, existing work treats the online mapping task as a local range perception task, overlooking the spatial scalability required for map construction. We propose IC-Mapper, an instance-centric online mapping framework, which comprises two primary components: 1) Instance-centric temporal association module: For the detection queries of adjacent frames, we measure them in both feature and geometric dimensions to obtain the matching correspondence between instances across frames. 2) Instance-centric spatial fusion module: We perform point sampling on the historical global map from a spatial dimension and integrate it with the detection results of instances corresponding to the current frame to achieve real-time expansion and update of the map. Based on the nuScenes dataset, we evaluate our approach on detection, tracking, and global mapping metrics. Experimental results demonstrate the superiority of IC-Mapper against other state-of-the-art methods. Code will be released on https://github.com/Brickzhuantou/IC-Mapper.

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Jiangtong Zhu, Zhao Yang, Yinan Shi, Jianwu Fang, Jianru Xue. 2025-03-05. IC-Mapper: Instance-Centric Spatio-Temporal Modeling for Online Vectorized Map Construction. https://arxiv.org/abs/2503.03882

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