3D Head-Position Prediction in First-Person View by Considering Head Pose for Human-Robot Eye Contact
This addresses the issue of mechanical delays in robots for eye contact during human movement, but it is incremental as it builds on existing prediction methods.
The paper tackled the problem of predicting a person's 3D head position from a first-person view to improve human-robot eye contact by considering head pose, and found that this approach was more accurate than a conventional Kalman filter-based method.
For a humanoid robot to make eye contact and initiate communication with a person, it is necessary to estimate the person's head position. However, eye contact becomes difficult due to the mechanical delay of the robot when the person is moving. Owing to these issues, it is important to conduct a head-position prediction to mitigate the effect of the delay in the robot motion. Based on the fact that humans turn their heads before changing direction while walking, we hypothesized that the accuracy of three-dimensional (3D) head-position prediction from a first-person view can be improved by considering the head pose. We compared our method with a conventional Kalman filter-based approach, and found our method to be more accurate. The experiment results show that considering the head pose helps improve the accuracy of 3D head-position prediction.