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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Series: | Mobile Information Systems |
Online Access: | http://dx.doi.org/10.1155/2016/9595306 |
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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 |
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