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arXiv · 2203.06911

Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations

Marvin Stuede·Moritz Schappler
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Abstract

This work presents a non-parametric spatio-temporal model for mapping human activity by mobile autonomous robots in a long-term context. Based on Variational Gaussian Process Regression, the model incorporates prior information of spatial and temporal-periodic dependencies to create a continuous representation of human occurrences. The inhomogeneous data distribution resulting from movements of the robot is included in the model via a heteroscedastic likelihood function and can be accounted for as predictive uncertainty. Using a sparse formulation, data sets over multiple weeks and several hundred square meters can be used for model creation. The experimental evaluation, based on multi-week data sets, demonstrates that the proposed approach outperforms the state of the art both in terms of predictive quality and subsequent path planning.

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Marvin Stuede, Moritz Schappler. 2022-03-14. Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations. https://arxiv.org/abs/2203.06911

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