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

verfasst von

Marvin Stuede, Moritz Schappler

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.

Organisationseinheit(en)

Robotik & autonome Systeme
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)

https://doi.org/10.48550/arXiv.2203.06911 (Zugang: Offen)
https://doi.org/10.1109/iros47612.2022.9982067 (Zugang: Geschlossen)