Bayesian Floor Field: Transferring people flow predictions across environments

Francesco Verdoja*, Tomasz Kucner, Ville Kyrki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

Mapping people dynamics is a crucial skill for robots, because it enables them to coexist in human-inhabited environments. However, learning a model of people dynamics is a time consuming process which requires observation of large amount of people moving in an environment. Moreover, approaches for mapping dynamics are unable to transfer the learned models across environments: each model is only able to describe the dynamics of the environment it has been built in. However, the impact of architectural geometry on people's movement can be used to anticipate their patterns of dynamics, and recent work has looked into learning maps of dynamics from occupancy. So far however, approaches based on trajectories and those based on geometry have not been combined. In this work we propose a novel Bayesian approach to learn people dynamics able to combine knowledge about the environment geometry with observations from human trajectories. An occupancy-based deep prior is used to build an initial transition model without requiring any observations of pedestrian; the model is then updated when observations become available using Bayesian inference. We demonstrate the ability of our model to increase data efficiency and to generalize across real large-scale environments, which is unprecedented for maps of dynamics.
Original languageEnglish
Title of host publication2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages12801-12807
Number of pages7
ISBN (Electronic)979-8-3503-7770-5
DOIs
Publication statusPublished - 14 Oct 2024
MoE publication typeA4 Conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - ADNEC, Abu Dhabi, United Arab Emirates
Duration: 14 Oct 202418 Oct 2024
https://iros2024-abudhabi.org/

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period14/10/202418/10/2024
Internet address

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