Details
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Pages | 126-133 |
| Number of pages | 8 |
| ISBN (electronic) | 978-1-6654-7927-1 |
| Publication status | Published - 2022 |
Publication series
| Name | IEEE International Conference on Intelligent Robots and Systems |
|---|---|
| Volume | 2022-October |
| ISSN (Print) | 2153-0858 |
| ISSN (electronic) | 2153-0866 |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Science Applications
Cite this
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2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2022. p. 126-133 (IEEE International Conference on Intelligent Robots and Systems; Vol. 2022-October).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations
AU - Stuede, Marvin
AU - Schappler, Moritz
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85146330760&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2203.06911
DO - 10.48550/arXiv.2203.06911
M3 - Conference contribution
SN - 978-1-6654-7928-8
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 126
EP - 133
BT - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
ER -