DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

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

Kaiser, Susanna and Garcia Puyol, Maria and Robertson, Patrick (2016) Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation. Mobile Information Systems, 2016. Hindawi Publishing Corporation. doi: 10.1155/2016/9595306. ISSN 1574-017X.

[img] PDF - Only accessible within DLR

Official URL: http://dx.doi.org/10.1155/2016/9595306


Indoor navigation and mapping has 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 noisy step measurements - addresses the indoor mapping and positioning problem and can provide accurate positioning in many structured indoor environments. FootSLAM estimates the posterior distributions of both human motion and the map of the environment which is a probabilistic representation of possible human motion at any location in an environment. These maps however, are themselves {\it uncertain} because they are estimated from noisy sensor observations. We believe that the uncertainty of a map and that of human motion can be represented based on information theory. 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 history 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 probabilistic maps.

Item URL in elib:https://elib.dlr.de/98472/
Document Type:Article
Title:Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kaiser, SusannaUNSPECIFIEDhttps://orcid.org/0000-0003-3210-6259UNSPECIFIED
Date:August 2016
Journal or Publication Title:Mobile Information Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Hindawi Publishing Corporation
Keywords:FootSLAM, Entropy, Maps, Uncertainty, Indoor Navigation, Pedestrian Dead Reckoning, Simultaneous Localization and Mapping
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication and Navigation
DLR - Research area:Raumfahrt
DLR - Program:R KN - Kommunikation und Navigation
DLR - Research theme (Project):R - Vorhaben GNSS2/Neue Dienste und Produkte (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation
Institute of Communication and Navigation > Communications Systems
Deposited By: Kaiser, Dr.-Ing. Susanna
Deposited On:17 Oct 2016 10:56
Last Modified:03 Nov 2023 07:37

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.