  Safe human-robot collaboration (HRC) has recently gained a lot of interest
with the emerging Industry 5.0 paradigm. Conventional robots are being replaced
with more intelligent and flexible collaborative robots (cobots). Safe and
efficient collaboration between cobots and humans largely relies on the cobot's
comprehensive semantic understanding of the dynamic surrounding of industrial
environments. Despite the importance of semantic understanding for such
applications, 3D semantic segmentation of collaborative robot workspaces lacks
sufficient research and dedicated datasets. The performance limitation caused
by insufficient datasets is called 'data hunger' problem. To overcome this
current limitation, this work develops a new dataset specifically designed for
this use case, named "COVERED", which includes point-wise annotated point
clouds of a robotic cell. Lastly, we also provide a benchmark of current
state-of-the-art (SOTA) algorithm performance on the dataset and demonstrate a
real-time semantic segmentation of a collaborative robot workspace using a
multi-LiDAR system. The promising results from using the trained Deep Networks
on a real-time dynamically changing situation shows that we are on the right
track. Our perception pipeline achieves 20Hz throughput with a prediction point
accuracy of $>$96\% and $>$92\% mean intersection over union (mIOU) while
maintaining an 8Hz throughput.
