Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Robot localization is a fundamental capability of all mobile robots. Because of uncertainties in acting and sensing and environmental factors such as people flocking around robots there is always the risk that a robot loses its localization. Very often behaviors of robots rely on a valid position estimation. Thus, for dependability of robot systems it is of great interest for the system to know the state of its localization component. In this paper we present an approach that allows a robot to asses if the localization is still valid. The approach assumes that the underlying localization approach is based on a particle filter. We use deep learning to identify temporal patterns in the particles in the case of losing/lost localization in combination with weak classifiers from the particle set and perception for boosted learning of a localization monitor. The approach is evaluated in a simulated transport robot environment where a degraded localization is provoked by disturbances cased by dynamic obstacles.
Original languageEnglish
Title of host publicationAdvances and Trends in Artificial Intelligence. From Theory to Practice
Subtitle of host publicationIEA/AIE 2019
EditorsFranz Wotawa, Gerhard Friedrich, Ingo Pill, Roxane Koitz-Hristov, Moonis Ali
PublisherSpringer, Cham
Pages700-713
Number of pages14
ISBN (Electronic)978-3-030-22999-3
ISBN (Print)978-3-030-22998-6
DOIs
Publication statusPublished - 15 Jun 2019
Event32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems - Graz, Austria
Duration: 9 Jul 201911 Jul 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11606

Conference

Conference32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems
Abbreviated titleIEA/AIE 2019
CountryAustria
CityGraz
Period9/07/1911/07/19

Fingerprint

Learning systems
Robots
Mobile robots
Classifiers

Cite this

Eder, M. J., Reip, M., & Steinbauer, G. (2019). Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization. In F. Wotawa, G. Friedrich, I. Pill, R. Koitz-Hristov, & M. Ali (Eds.), Advances and Trends in Artificial Intelligence. From Theory to Practice: IEA/AIE 2019 (pp. 700-713). (Lecture Notes in Computer Science; Vol. 11606). Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_60

Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization. / Eder, Matthias Josef; Reip, Michael; Steinbauer, Gerald.

Advances and Trends in Artificial Intelligence. From Theory to Practice: IEA/AIE 2019. ed. / Franz Wotawa; Gerhard Friedrich; Ingo Pill; Roxane Koitz-Hristov; Moonis Ali. Springer, Cham, 2019. p. 700-713 (Lecture Notes in Computer Science; Vol. 11606).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Eder, MJ, Reip, M & Steinbauer, G 2019, Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization. in F Wotawa, G Friedrich, I Pill, R Koitz-Hristov & M Ali (eds), Advances and Trends in Artificial Intelligence. From Theory to Practice: IEA/AIE 2019. Lecture Notes in Computer Science, vol. 11606, Springer, Cham, pp. 700-713, 32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, Graz, Austria, 9/07/19. https://doi.org/10.1007/978-3-030-22999-3_60
Eder MJ, Reip M, Steinbauer G. Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization. In Wotawa F, Friedrich G, Pill I, Koitz-Hristov R, Ali M, editors, Advances and Trends in Artificial Intelligence. From Theory to Practice: IEA/AIE 2019. Springer, Cham. 2019. p. 700-713. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-22999-3_60
Eder, Matthias Josef ; Reip, Michael ; Steinbauer, Gerald. / Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization. Advances and Trends in Artificial Intelligence. From Theory to Practice: IEA/AIE 2019. editor / Franz Wotawa ; Gerhard Friedrich ; Ingo Pill ; Roxane Koitz-Hristov ; Moonis Ali. Springer, Cham, 2019. pp. 700-713 (Lecture Notes in Computer Science).
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