RT Journal Article
T1 Human 3D Pose Estimation with a Tilting Camera for Social Mobile Robot Interaction.
A1 Garcia-Salguero, Mercedes
A1 Gonzalez-Jimenez, Javier
A1 Moreno, Francisco-Angel
K1 3D computer vision
K1 OpenPose
K1 RGB-D cameras
K1 camera pose calibration
K1 human body pose estimation
K1 human–robot interaction
AB Human-Robot interaction represents a cornerstone of mobile robotics, especially within the field of social robots. In this context, user localization becomes of crucial importance for the interaction. This work investigates the capabilities of wide field-of-view RGB cameras to estimate the 3D position and orientation (i.e., the pose) of a user in the environment. For that, we employ a social robot endowed with a fish-eye camera hosted in a tilting head and develop two complementary approaches: (1) a fast method relying on a single image that estimates the user pose from the detection of their feet and does not require either the robot or the user to remain static during the reconstruction; and (2) a method that takes some views of the scene while the camera is being tilted and does not need the feet to be visible. Due to the particular setup of the tilting camera, special equations for 3D reconstruction have been developed. In both approaches, a CNN-based skeleton detector (OpenPose) is employed to identify humans within the image. A set of experiments with real data validate our two proposed methods, yielding similar results than commercial RGB-D cameras while surpassing them in terms of coverage of the scene (wider FoV and longer range) and robustness to light conditions.
YR 2019
FD 2019-11-13
LK http://hdl.handle.net/10668/14740
UL http://hdl.handle.net/10668/14740
LA en
DS RISalud
RD Apr 15, 2025