Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation

Indoor navigation and mapping have recently become an important field of interest for researchers because global navigation satellite systems (GNSS) are very often unavailable inside buildings. FootSLAM, a SLAM (Simultaneous Localization and Mapping) algorithm for pedestrians based on step measureme...

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Main Authors: Susanna Kaiser, Maria Garcia Puyol, Patrick Robertson
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Mobile Information Systems
Online Access:http://dx.doi.org/10.1155/2016/9595306
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spelling doaj-3f3bfcdd16a84384800b06ffe217cce52021-07-02T02:34:41ZengHindawi LimitedMobile Information Systems1574-017X1875-905X2016-01-01201610.1155/2016/95953069595306Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor NavigationSusanna Kaiser0Maria Garcia Puyol1Patrick Robertson2German Aerospace Center (DLR), Institute for Communications and Navigation, Oberpfaffenhofen, GermanyGerman Aerospace Center (DLR), Institute for Communications and Navigation, Oberpfaffenhofen, GermanyGerman Aerospace Center (DLR), Institute for Communications and Navigation, Oberpfaffenhofen, GermanyIndoor navigation and mapping have recently become an important field of interest for researchers because global navigation satellite systems (GNSS) are very often unavailable inside buildings. FootSLAM, a SLAM (Simultaneous Localization and Mapping) algorithm for pedestrians based on step measurements, addresses the indoor mapping and positioning problem and can provide accurate positioning in many structured indoor environments. In this paper, we investigate how to compare FootSLAM maps via two entropy metrics. Since collaborative FootSLAM requires the alignment and combination of several individual FootSLAM maps, we also investigate measures that help to align maps that partially overlap. We distinguish between the map entropy conditioned on the sequence of pedestrian’s poses, which is a measure of the uncertainty of the estimated map, and the entropy rate of the pedestrian’s steps conditioned on the history of poses and conditioned on the estimated map. Because FootSLAM maps are built on a hexagon grid, the entropy and relative entropy metrics are derived for the special case of hexagonal transition maps. The entropy gives us a new insight on the performance of FootSLAM’s map estimation process.http://dx.doi.org/10.1155/2016/9595306
collection DOAJ
language English
format Article
sources DOAJ
author Susanna Kaiser
Maria Garcia Puyol
Patrick Robertson
spellingShingle Susanna Kaiser
Maria Garcia Puyol
Patrick Robertson
Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation
Mobile Information Systems
author_facet Susanna Kaiser
Maria Garcia Puyol
Patrick Robertson
author_sort Susanna Kaiser
title Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation
title_short Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation
title_full Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation
title_fullStr Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation
title_full_unstemmed Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation
title_sort measuring the uncertainty of probabilistic maps representing human motion for indoor navigation
publisher Hindawi Limited
series Mobile Information Systems
issn 1574-017X
1875-905X
publishDate 2016-01-01
description Indoor navigation and mapping have recently become an important field of interest for researchers because global navigation satellite systems (GNSS) are very often unavailable inside buildings. FootSLAM, a SLAM (Simultaneous Localization and Mapping) algorithm for pedestrians based on step measurements, addresses the indoor mapping and positioning problem and can provide accurate positioning in many structured indoor environments. In this paper, we investigate how to compare FootSLAM maps via two entropy metrics. Since collaborative FootSLAM requires the alignment and combination of several individual FootSLAM maps, we also investigate measures that help to align maps that partially overlap. We distinguish between the map entropy conditioned on the sequence of pedestrian’s poses, which is a measure of the uncertainty of the estimated map, and the entropy rate of the pedestrian’s steps conditioned on the history of poses and conditioned on the estimated map. Because FootSLAM maps are built on a hexagon grid, the entropy and relative entropy metrics are derived for the special case of hexagonal transition maps. The entropy gives us a new insight on the performance of FootSLAM’s map estimation process.
url http://dx.doi.org/10.1155/2016/9595306
work_keys_str_mv AT susannakaiser measuringtheuncertaintyofprobabilisticmapsrepresentinghumanmotionforindoornavigation
AT mariagarciapuyol measuringtheuncertaintyofprobabilisticmapsrepresentinghumanmotionforindoornavigation
AT patrickrobertson measuringtheuncertaintyofprobabilisticmapsrepresentinghumanmotionforindoornavigation
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