A Robust Filter for Marker-less Multi-person Tracking in Human-Robot Interaction Scenarios

TL;DR

This paper introduces a filtering method that improves multi-person tracking accuracy in human-robot interaction by refining 3D human poses from RGB-D data, reducing errors caused by pose estimation inaccuracies.

Contribution

The paper presents a novel filtering pipeline that enhances marker-less multi-person tracking robustness in HRI scenarios, addressing occlusions and pose estimation errors.

Findings

More consistent and noise-free human motion representation

Reduced robot jittering and unexpected movements

Enabled smoother and more reliable human-robot interactions

Abstract

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.