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

Sun, Li and Yan, Zhi and Molina Mellado, Sergi and Hanheide, Marc and Duckett, Tom (2018) 3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data. International Conference on Robotics and Automation (ICRA) 2018 . ISSN 0891-060X

Full content URL: https://doi.org/10.1109/ICRA.2018.8461228

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

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Abstract

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 deployments and aims to learn context-dependent (environment- and time-specific) human activities. Our approach incorporates long-term temporal information (i.e. date and time) with short-term pose observations as input. A sequence-to-sequence LSTM encoder-decoder is trained, which encodes observations into LSTM and then decodes the resulting predictions. On deployment, the approach can perform on-the-fly prediction in real-time. Instead of using manually annotated data, we rely on a robust human detection, tracking and SLAM system, providing us with examples in a global coordinate system. We validate the approach using more than 15 km of pedestrian trajectories recorded in a care home environment over a period of three months. The experiments show that the proposed T-PoseLSTM model outperforms the state-of-the-art 2D-based method for human trajectory prediction in long-term mobile robot deployments.

Keywords:trajectory prediction, people tracking, LIDAR data, service robots, 3D Lidar human detection
Subjects:H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:31956
Deposited On:02 Jul 2018 14:50

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