  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 as predictions. For deployment, it 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 15K pedestrian
trajectories recorded in a care home environment over a period of three months.
The experiment shows that the proposed T-Pose-LSTM model advances the
state-of-the-art 2D-based method for human trajectory prediction in long-term
mobile robot deployments.
