基于视觉数据实时构建矢量地图，解决传统方法效率低问题
IC-Mapper: Instance-Centric Spatio-Temporal Modeling for Online Vectorized Map Construction
- 以实例为中心设计时序关联与空间融合机制
- 在nuScenes上实现更优的检测、跟踪与全局地图性能
- 适合自动驾驶高精地图实时构建场景
基于视觉数据的在线矢量地图构建可跳过传统地图制作中的数据采集、后期处理和人工标注流程，显著提升建图效率。然而，现有方法将在线映射任务视为局部感知任务，忽视了地图构建所需的时空可扩展性。本文提出IC-Mapper，一种以实例为中心的在线映射框架，包含两个核心模块：1）实例中心时序关联模块：对相邻帧的检测查询在特征与几何维度同时度量，获取跨帧实例匹配关系；2）实例中心空间融合模块：从历史全局地图中进行点采样，并与当前帧对应实例的检测结果融合，实现地图的实时扩展与更新。基于nuScenes数据集，在检测、追踪与全局地图指标上评估，实验结果表明IC-Mapper优于现有最先进方法。代码将发布于https://github.com/Brickzhuantou/IC-Mapper。
原文摘要 · Abstract (English)
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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