Use of Thermal Point Cloud for Thermal Comfort Measurement and Human Pose Estimation in Robotic Monitoring

摘要

This paper describes applications of thermal point cloud to lifestyle support robots. 3D information is useful for recognizing human and objects based on their shapes, while thermal information is useful for assessing the residential and the human states as well as for detecting human. Combining these two kinds of information will be beneficial to the robots which live with and support people at home or in care houses. This paper shows two applications of thermal point cloud. One is thermal comfort measurement based on predictive mean vote (PMV) which uses, as one of the factors, the amount of clothing estimated by thermal information. The other is human pose estimation only by depth images, which has an advantages in terms of privacy and insensitivity to illumination changes. We developed methods for these applications and show experimental results.