A Robust Filter for Marker-less Multi-person Tracking in Human-Robot Interaction Scenarios
This work addresses the challenge of improving user trust and experience in collaborative robotics by mitigating robot jittering, though it appears incremental as it refines existing methods rather than introducing a new paradigm.
The paper tackled the problem of marker-less multi-person tracking in human-robot interaction by addressing errors from human pose estimation and depth cameras, resulting in more consistent motion representation and reduced unexpected robot movements for smoother interaction.
Pursuing natural and marker-less human-robot interaction (HRI) has been a long-standing robotics research focus, driven by the vision of seamless collaboration without physical markers. Marker-less approaches promise an improved user experience, but state-of-the-art struggles with the challenges posed by intrinsic errors in human pose estimation (HPE) and depth cameras. These errors can lead to issues such as robot jittering, which can significantly impact the trust users have in collaborative systems. We propose a filtering pipeline that refines incomplete 3D human poses from an HPE backbone and a single RGB-D camera to address these challenges, solving for occlusions that can degrade the interaction. Experimental results show that using the proposed filter leads to more consistent and noise-free motion representation, reducing unexpected robot movements and enabling smoother interaction.