Robots are increasingly present in our lives, sharing the workspace and tasks with human co-workers. However, existing interfaces for human-robot interaction / cooperation (HRI/C) have limited levels of intuitiveness to use and safety is a major concern when humans and robots share the same workspace. Many times, this is due to the lack of a reliable estimation of the human pose in space which is the primary input to calculate the human-robot minimum distance (required for safety and collision avoidance) and HRI/C featuring machine learning algorithms classifying human behaviours / gestures. Each sensor type has its own characteristics resulting in problems such as occlusions (vision) and drift (inertial) when used in an isolated fashion. In this paper, it is proposed a combined system that merges the human tracking provided by a 3D vision sensor with the pose estimation provided by a set of inertial measurement units (IMUs) placed in human body limbs. The IMUs compensate the gaps in occluded areas to have tracking continuity. To mitigate the lingering effects of the IMU offset we propose a continuous online calculation of the offset value. Experimental tests were designed to simulate human motion in a human-robot collaborative environment where the robot moves away to avoid unexpected collisions with de human. Results indicate that our approach is able to capture the human\textsc's position, for example the forearm, with a precision in the millimetre range and robustness to occlusions.

翻译：机器人日益融入我们的生活，与人共享工作空间和任务。然而，现有的人机交互/协作界面在易用性方面有限，且当人与机器人共享同一工作空间时，安全是一个主要问题。这往往源于缺乏可靠的人体空间姿态估计，而该估计是计算人机最小距离（安全与避碰所需）及基于机器学习算法的人机交互/协作（需分类人体行为/手势）的主要输入。每种传感器类型各有特性，单独使用时会导致诸如遮挡（视觉传感器）和漂移（惯性传感器）等问题。本文提出一种组合系统，将3D视觉传感器提供的人体跟踪与置于人体四肢的惯性测量单元提供的姿态估计相融合。惯性测量单元补偿遮挡区域的缺失，实现跟踪连续性。为减轻惯性测量单元偏移的残留影响，我们提出一种持续的偏移量在线计算方法。设计了模拟人机协作环境中人体运动的实验测试，其中机器人移动以避开与人的意外碰撞。结果表明，我们的方法能够以毫米级精度捕捉人体（如前臂）位置，并对遮挡具有鲁棒性。