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.

翻译：安全的人机协作（HRC）随着新兴的工业5.0范式而备受关注。传统机器人正被更智能、更灵活的协作机器人所取代。机器人与人类的安全高效协作很大程度上依赖于协作机器人对工业环境动态变化场景的全面语义理解。尽管语义理解对此类应用至关重要，但协作机器人工作空间的三维语义分割仍缺乏充分的研究和专用数据集。由数据集不足导致的性能限制被称为"数据饥饿"问题。为突破这一瓶颈，本研究专门针对该应用场景开发了名为"COVERED"的新数据集，其中包含机器人单元的点级标注点云。最后，我们还提供了当前最优算法在该数据集上的性能基准，并展示了基于多激光雷达系统的协作机器人工作空间实时语义分割。将训练后的深度网络应用于实时动态变化场景所取得的令人振奋的结果表明，我们正沿着正确方向前进。我们提出的感知流水线在保持8Hz吞吐量的同时，实现了20Hz的处理速度，预测点精度达$>$96%，平均交并比达$>$92%。