3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data

@article{Sun20173DOFPT,
  title={3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data},
  author={Li Sun and Zhi Yan and Sergi Molina Mellado and Marc Hanheide and Tom Duckett},
  journal={2018 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2017},
  pages={1-7}
}
  • Li SunZhi Yan T. Duckett
  • Published 30 September 2017
  • Computer Science
  • 2018 IEEE International Conference on Robotics and Automation (ICRA)
This paper presents a novel 3DOF pedestrian trajectory prediction approach for autonomous mobile service robots. While most previously reported methods are based on learning of 2D positions in monocular camera images, our approach uses range-finder sensors to learn and predict 3DOF pose trajectories (i.e. 2D position plus 1D rotation within the world coordinate system). Our approach, T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using long-term data from real-world robot… 

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