Automated Multimodal Data Annotation via Calibration With Indoor
  Positioning System
Ryan Rubel Andrew Dudash Mohammad Goli James O'Hara Karl Wunderlich
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%.