As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot.
@article{arxiv.2310.05600,
title = {Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments},
author = {Michael G. Adam and Sebastian Eger and Martin Piccolrovazzi and Maged Iskandar and Joern Vogel and Alexander Dietrich and Seongjien Bien and Jon Skerlj and Abdeldjallil Naceri and Eckehard Steinbach and Alin Albu-Schaeffer and Sami Haddadin and Wolfram Burgard},
journal= {arXiv preprint arXiv:2310.05600},
year = {2023}
}