Enhanced Human-Machine Interaction by Combining Proximity Sensing with Global Perception
This work addresses the need for enhanced human-machine interaction in collaborative robotics by improving pose estimation accuracy and reducing occlusions in large workspaces, though it appears incremental as it builds on existing sensor technologies.
The paper tackled the problem of capturing human pose in large collaborative workspaces by developing an optical system using a single panoramic color camera to predict metric 3D pose over a larger field of view than active depth sensors, merging posture context with proximity perception to reduce occlusions and improve accuracy at long distances, as demonstrated in use cases with multiple humans and robots.
The raise of collaborative robotics has led to wide range of sensor technologies to detect human-machine interactions: at short distances, proximity sensors detect nontactile gestures virtually occlusion-free, while at medium distances, active depth sensors are frequently used to infer human intentions. We describe an optical system for large workspaces to capture human pose based on a single panoramic color camera. Despite the two-dimensional input, our system is able to predict metric 3D pose information over larger field of views than would be possible with active depth measurement cameras. We merge posture context with proximity perception to reduce occlusions and improve accuracy at long distances. We demonstrate the capabilities of our system in two use cases involving multiple humans and robots.