Improving Gaussian Processes based mapping of wireless signals using path loss models

Renato Miyagusuku, Atsushi Yamashita, Hajime Asama

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

24 Scopus citations

Abstract

Indoor robot localization systems using wireless signal measurements have gained popularity in recent years, as wireless Local Area Networks can be found practically everywhere. In this field, a popular approach is the use of fingerprinting techniques, such as Gaussian Processes. In our approach, we improve Gaussian Processes based mapping using path loss models as priors. Path loss models encode information regarding the signal propagation phenomena into the mapping. Our approach first fits training data to a simple path loss model, and then trains a zero-mean Gaussian Process with the mismatches between the models and the data. Signal strength mean predictions are done using both the path loss model and the Gaussian Process output, while variances are calculated by bounding the Gaussian Process variance using the path loss models. Notably, the main improvement generated by our approach is not an enhanced mean value prediction, but rather a better model variance prediction. This translates into better likelihood estimations, leading to higher localization accuracy. Experiments using data acquired in an indoor environment and our approach as the perceptual likelihood of a dual Monte Carlo localization algorithm are used to demonstrate this improvement. Furthermore, this idea can be extrapolated to other fingerprinting techniques and to applications other than wireless-based localization.

Original languageEnglish
Title of host publicationIROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4610-4615
Number of pages6
ISBN (Electronic)9781509037629
DOIs
StatePublished - 28 Nov 2016
Externally publishedYes
Event2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 - Daejeon, Korea, Republic of
Duration: 9 Oct 201614 Oct 2016

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2016-November
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Country/TerritoryKorea, Republic of
CityDaejeon
Period9/10/1614/10/16

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