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%.

通过融合 LiDAR 和相机数据的学习目标检测方法需要带有标签的训练样本，然而某些特定应用（如仓储机器人或自动化基础设施）需要在现有数据集中没有的语义类别。因此，为了促进快速创建多模态目标检测数据集并减轻人工标注的负担，我们提出了一种新颖的自动标注流程。我们的方法使用室内定位系统（IPS）为点云和图像生成准确的检测标签，完全消除了手动标注的需求。在一项实验中，该系统比与人类基准相比，快速标注感兴趣的物体的速度提高了 261.8 倍，并将端到端数据集创建速度加快了 61.5%。