Automated Multimodal Data Annotation via Calibration With Indoor Positioning System
This addresses the need for rapid dataset creation in niche domains like warehouse robotics, though it is incremental as it builds on existing annotation methods.
The paper tackles the problem of creating labeled multimodal datasets for niche applications by proposing an automated annotation pipeline using an indoor positioning system, which annotates objects 261.8 times faster than human labeling and speeds up dataset creation by 61.5%.
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%.