In recent years, the effective and safe collaboration between humans and machines has gained significant importance, particularly in the Industry 4.0 scenario. A critical prerequisite for realizing this collaborative paradigm is precisely understanding the robot's 3D pose within its environment. Therefore, in this paper, we introduce a novel vision-based system leveraging depth data to accurately establish the 3D locations of robotic joints. Specifically, we prove the ability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. Indeed, we introduce the concept of Pose Nowcasting, denoting the capability of a system to exploit the learned knowledge of the future to improve the estimation of the present. The experimental evaluation is conducted on two different datasets, providing state-of-the-art and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.

翻译：近年来，人类与机器之间有效且安全的协作变得尤为重要，尤其是在工业4.0场景中。实现这种协作范式的关键前提是精确理解机器人在其环境中的3D姿态。因此，本文提出了一种基于视觉的新型系统，利用深度数据准确确定机器人关节的3D位置。具体而言，我们证明了所提系统能够通过联合学习预测未来姿态，从而提升当前姿态估计的准确性。实际上，我们引入了“姿态现时预测”的概念，指系统利用习得的未来知识改进当前估计的能力。实验评估在两个不同数据集上进行，提供了最先进且实时的性能，并在机器人和人类场景中均验证了所提方法的有效性。