论文标题
咬合刺激性多敏感性姿势估计在物理人类机器人相互作用中
Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction
论文作者
论文摘要
3D姿势估计对于分析和改善物理人类机器人相互作用的人体工程学和降低肌肉骨骼疾病的风险很重要。基于视觉的姿势估计方法容易出现传感器和模型误差以及遮挡，而姿势估计仅来自相互作用的机器人的轨迹，却来自模棱两可的解决方案。为了从两种方法的优势中受益并改善了它们的弊端，我们引入了低成本，非侵入性和遮挡式多敏感性3D姿势估计算法中的物理人类人类机器人相互作用。我们在单个相机上使用openpose的2D姿势，以及人类执行任务时相互作用的机器人的轨迹。我们将问题建模为部分观察的动力学系统，并通过粒子滤波器推断3D姿势。我们介绍了远程操作的工作，但可以将其推广到其他人类机器人互动的其他应用。我们表明，我们的多感官系统比仅使用openpose或姿势估计仅使用机器人的轨迹估算姿势估计的姿势估计更好地解决了人运动冗余。与金标准运动捕获姿势相比，这将提高估计姿势的准确性。此外，当使用Rula评估工具进行姿势评估时，我们的方法还比其他单一感觉方法更好。
3D posture estimation is important in analyzing and improving ergonomics in physical human-robot interaction and reducing the risk of musculoskeletal disorders. Vision-based posture estimation approaches are prone to sensor and model errors, as well as occlusion, while posture estimation solely from the interacting robot's trajectory suffers from ambiguous solutions. To benefit from the advantages of both approaches and improve upon their drawbacks, we introduce a low-cost, non-intrusive, and occlusion-robust multi-sensory 3D postural estimation algorithm in physical human-robot interaction. We use 2D postures from OpenPose over a single camera, and the trajectory of the interacting robot while the human performs a task. We model the problem as a partially-observable dynamical system and we infer the 3D posture via a particle filter. We present our work in teleoperation, but it can be generalized to other applications of physical human-robot interaction. We show that our multi-sensory system resolves human kinematic redundancy better than posture estimation solely using OpenPose or posture estimation solely using the robot's trajectory. This will increase the accuracy of estimated postures compared to the gold-standard motion capture postures. Moreover, our approach also performs better than other single sensory methods when postural assessment using RULA assessment tool.
