Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics |
| Untertitel | ICINCO 2022 |
| Herausgeber/-innen | Giuseppina Gini, Henk Nijmeijer, Wolfram Burgard, Dimitar P. Filev |
| Seiten | 296-303 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 19th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2022 - Lisbon, Portugal Dauer: 14 Juli 2022 → 16 Juli 2022 |
Publikationsreihe
| Name | ICINCO International Conference on Informatics in Control, Automation and Robotic |
|---|---|
| Band | 1 |
| ISSN (Print) | 2184-2809 |
Abstract
Being able to provide trustworthy localization for a robot in a map is essential for various tasks with safety-related requirements. In contrast to classical probabilistic approaches that represent the uncertainty as a Gaussian distribution, we use interval error bounds for the uncertainty estimation of a localization problem. To tackle and identify the limitations of probabilistic localization uncertainty estimation, we carry out comparison experiments between an interval-based method and a factor graph-based probabilistic method. Different measurement error models are propagated by the two methods to derive the robot pose uncertainty estimates. Results show that the probabilistic approach can provide very good pose uncertainty when there is no non-Gaussian systematic sensor error. However, if the measurements have unmodeled systematic errors, the interval approach is able to robustly contain the true poses whereas the probabilistic approach gives completely wrong results.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Signalverarbeitung
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Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics: ICINCO 2022. Hrsg. / Giuseppina Gini; Henk Nijmeijer; Wolfram Burgard; Dimitar P. Filev. 2022. S. 296-303 (ICINCO International Conference on Informatics in Control, Automation and Robotic; Band 1).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Interval-based Robot Localization with Uncertainty Evaluation
AU - Jiang, Yuehan
AU - Ehambram, Aaronkumar
AU - Wagner, Bernardo
N1 - Funding Information: This work was supported by the German Academic Exchange Service (DAAD) as part of the Research Training Group i.c.sens [RTG 2159].
PY - 2022
Y1 - 2022
N2 - Being able to provide trustworthy localization for a robot in a map is essential for various tasks with safety-related requirements. In contrast to classical probabilistic approaches that represent the uncertainty as a Gaussian distribution, we use interval error bounds for the uncertainty estimation of a localization problem. To tackle and identify the limitations of probabilistic localization uncertainty estimation, we carry out comparison experiments between an interval-based method and a factor graph-based probabilistic method. Different measurement error models are propagated by the two methods to derive the robot pose uncertainty estimates. Results show that the probabilistic approach can provide very good pose uncertainty when there is no non-Gaussian systematic sensor error. However, if the measurements have unmodeled systematic errors, the interval approach is able to robustly contain the true poses whereas the probabilistic approach gives completely wrong results.
AB - Being able to provide trustworthy localization for a robot in a map is essential for various tasks with safety-related requirements. In contrast to classical probabilistic approaches that represent the uncertainty as a Gaussian distribution, we use interval error bounds for the uncertainty estimation of a localization problem. To tackle and identify the limitations of probabilistic localization uncertainty estimation, we carry out comparison experiments between an interval-based method and a factor graph-based probabilistic method. Different measurement error models are propagated by the two methods to derive the robot pose uncertainty estimates. Results show that the probabilistic approach can provide very good pose uncertainty when there is no non-Gaussian systematic sensor error. However, if the measurements have unmodeled systematic errors, the interval approach is able to robustly contain the true poses whereas the probabilistic approach gives completely wrong results.
KW - Factor Graph
KW - Interval Analysis
KW - Landmark-based Localization
KW - Probabilistic Uncertainty
KW - Uncertainty Estimation
UR - http://www.scopus.com/inward/record.url?scp=85176013975&partnerID=8YFLogxK
U2 - 10.5220/0011143700003271
DO - 10.5220/0011143700003271
M3 - Conference contribution
AN - SCOPUS:85176013975
SN - 9789897585852
T3 - ICINCO International Conference on Informatics in Control, Automation and Robotic
SP - 296
EP - 303
BT - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics
A2 - Gini, Giuseppina
A2 - Nijmeijer, Henk
A2 - Burgard, Wolfram
A2 - Filev, Dimitar P.
T2 - 19th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2022
Y2 - 14 July 2022 through 16 July 2022
ER -