Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction
This work addresses occlusion and ambiguity issues in posture estimation for ergonomics and safety in human-robot interaction, though it is incremental as it builds on existing methods like OpenPose and particle filters.
The paper tackles the problem of 3D posture estimation in physical human-robot interaction by combining 2D postures from a camera and robot trajectory data, resulting in improved accuracy over single-sensory methods as shown by better resolution of kinematic redundancy and performance in RULA assessment.
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.