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Automatic parameter tuning in localization algorithms
Linköping University, Department of Computer and Information Science, Software and Systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Automatisk parameterjustering av lokaliseringsalgoritmer (Swedish)
Abstract [en]

Many algorithms today require a number of parameters to be set in order to perform well in a given application. The tuning of these parameters is often difficult and tedious to do manually, especially when the number of parameters is large. It is also unlikely that a human can find the best possible solution for difficult problems. To be able to automatically find good sets of parameters could both provide better results and save a lot of time.

The prominent methods Bayesian optimization and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are evaluated for automatic parameter tuning in localization algorithms in this work. Both methods are evaluated using a localization algorithm on different datasets and compared in terms of computational time and the precision and recall of the final solutions. This study shows that it is feasible to automatically tune the parameters of localization algorithms using the evaluated methods. In all experiments performed in this work, Bayesian optimization was shown to make the biggest improvements early in the optimization but CMA-ES always passed it and proceeded to reach the best final solutions after some time. This study also shows that automatic parameter tuning is feasible even when using noisy real-world data collected from 3D cameras.

Place, publisher, year, edition, pages
2019. , p. 57
Keywords [en]
cma-es, covariance matrix adaptation, evolution strategies, bayesian optimization, black box optimization, object localization, 3d localization, bin picking, parameter tuning, algorithm configuration
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-158132ISRN: LIU-IDA/LITH-EX-A--19/052--SEOAI: oai:DiVA.org:liu-158132DiVA, id: diva2:1330445
External cooperation
SICK IVP AB
Subject / course
Computer Engineering
Presentation
2019-06-12, Allen Newell, Linköping, 08:15 (English)
Supervisors
Examiners
Available from: 2019-06-27 Created: 2019-06-25 Last updated: 2019-06-27Bibliographically approved

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CiteExportLink to record
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Citation style
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