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Human 3D Pose Estimation with a Tilting Camera for Social Mobile Robot Interaction.

Mercedes Garcia-Salguero1, Javier Gonzalez-Jimenez1, Francisco-Angel Moreno1

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Summary

This study introduces two novel methods for estimating human 3D pose using wide field-of-view cameras on social robots. These approaches offer improved scene coverage and robustness compared to commercial RGB-D cameras.

Keywords:
3D computer visionOpenPoseRGB-D camerascamera pose calibrationhuman body pose estimationhuman–robot interaction

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Area of Science:

  • Robotics
  • Computer Vision
  • Human-Robot Interaction

Background:

  • User localization is critical for effective human-robot interaction in social robotics.
  • Wide field-of-view (FoV) cameras offer potential for enhanced environmental awareness.

Purpose of the Study:

  • To investigate the use of wide FoV RGB cameras for estimating user 3D pose (position and orientation).
  • To develop and validate two complementary user localization methods for social robots.

Main Methods:

  • Developed two methods: (1) a single-image approach using feet detection, and (2) a multi-view approach utilizing camera tilting.
  • Employed a CNN-based skeleton detector (OpenPose) for human identification.
  • Derived specialized 3D reconstruction equations for a tilting fish-eye camera system.

Main Results:

  • Both proposed methods achieved comparable results to commercial RGB-D cameras.
  • The methods demonstrated superior scene coverage (wider FoV, longer range) and robustness to lighting variations.
  • Validated through experiments using real-world data.

Conclusions:

  • Wide FoV RGB cameras are effective for user pose estimation in social robotics.
  • The developed methods provide a robust and comprehensive solution for user localization, enhancing human-robot interaction capabilities.