CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes
We present CoMet, a novel approach for computing a group's cohesion and using that to improve a robot's navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of ped...
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Zusammenfassung: | We present CoMet, a novel approach for computing a group's cohesion and using
that to improve a robot's navigation in crowded scenes. Our approach uses a
novel cohesion-metric that builds on prior work in social psychology. We
compute this metric by utilizing various visual features of pedestrians from an
RGB-D camera on-board a robot. Specifically, we detect characteristics
corresponding to proximity between people, their relative walking speeds, the
group size, and interactions between group members. We use our cohesion-metric
to design and improve a navigation scheme that accounts for different levels of
group cohesion while a robot moves through a crowd. We evaluate the precision
and recall of our cohesion-metric based on perceptual evaluations. We highlight
the performance of our social navigation algorithm on a Turtlebot robot and
demonstrate its benefits in terms of multiple metrics: freezing rate (57%
decrease), deviation (35.7% decrease), and path length of the trajectory(23.2%
decrease). |
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DOI: | 10.48550/arxiv.2108.09848 |