Time-varying Pedestrian Flow Models for Service Robots

Tomas Vintr¹, Tomáš Krajník¹, Sergi Molina², Ransalu Senanayake³, George Broughton¹, Zhi Yan4, Jiri Ulrich¹, Tomasz Kucner⁵, Chittaranjan Swaminathan⁵, Filip Majer¹, Maria Stachova⁶, and Achim Lilienthal^5
1Artificial Intelligence Centre, Czech Technical University, Czechia
²Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, UK, United Kingdom
³Stanford University, United States
⁴Distributed Artificial Intelligence and Knowledge Laboratory, University of Technology of Belfort-Montbeliard, France
⁵AASS Mobile Robotics and Olfaction Lab, Örebro University, Sweden, Sweden
⁶University of Matej Bel in Banska Bystrica, Slovakia, Slovakia

We present a human-centric spatiotemporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples’ routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.