Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations
Abstract
This work presents a non-parametric spatiotemporal 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.
Details
- Organisationseinheit(en)
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Robotic Systems
Institut für Mechatronische Systeme
- Typ
- Aufsatz in Konferenzband
- Seiten
- 126-133
- Anzahl der Seiten
- 8
- Publikationsdatum
- 2022
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Software, Steuerungs- und Systemtechnik, Maschinelles Sehen und Mustererkennung, Angewandte Informatik
- Elektronische Version(en)
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https://doi.org/10.48550/arXiv.2203.06911 (Zugang:
Offen
)
https://doi.org/10.1109/iros47612.2022.9982067 (Zugang: Geschlossen )