通过室内定位系统校准实现多模态数据自动标注
Automated Multimodal Data Annotation via Calibration With Indoor Positioning System
AI总结：
针对小众场景缺乏多模态检测标注数据的问题，提出利用室内定位系统自动生成点云和图像标签的流程，标注速度达人工 261.8 倍，端到端建集提速 61.5%。
摘要：
基于 LiDAR 与相机数据融合的学习型目标检测方法需要带标注的训练样本，但仓库机器人或自动化基础设施等小众应用所需的语义类别在现有大型数据集中并不存在。因此，为促进多模态目标检测数据集的快速创建并减轻人工标注负担，我们提出一种新型自动标注流程。该方法利用室内定位系统（IPS）为点云和图像生成准确的检测标签，并完全消除人工标注。实验中，该系统对感兴趣目标的标注速度是人类基线的 261.8 倍，并使端到端数据集创建速度提升 61.5%。
英文摘要：
Learned object detection methods based on fusion of LiDAR and camera data require labeled training samples, but niche applications, such as warehouse robotics or automated infrastructure, require semantic classes not available in large existing datasets. Therefore, to facilitate the rapid creation of multimodal object detection datasets and alleviate the burden of human labeling, we propose a novel automated annotation pipeline. Our method uses an indoor positioning system (IPS) to produce accurate detection labels for both point clouds and images and eliminates manual annotation entirely. In an experiment, the system annotates objects of interest 261.8 times faster than a human baseline and speeds up end-to-end dataset creation by 61.5%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
