From 9fb512ef3df98e223a4b6ddf89d442676d9cf149 Mon Sep 17 00:00:00 2001 From: toni Date: Mon, 16 Jul 2018 10:10:07 +0200 Subject: [PATCH] final abstract --- tex/bare_conf.tex | 2 +- tex/chapters/abstract.tex | 20 ++++++++++++++++++-- 2 files changed, 19 insertions(+), 3 deletions(-) diff --git a/tex/bare_conf.tex b/tex/bare_conf.tex index f3c3b77..d9db3a3 100644 --- a/tex/bare_conf.tex +++ b/tex/bare_conf.tex @@ -43,7 +43,7 @@ % Authors, for metadata in PDF \AuthorNames{Toni Fetzer, Frank Ebner, Markus Bullmann, Frank Deinzer and Marcin Grzegorzek} -\keyword{todo} +\keyword{indoor localization; Wi-Fi; PDR; sensor fusion; smartphone; particle filter; sample impoverishment; estimation; historic buildings; navigation mesh} % Affiliations / Addresses (Add [1] after \address if there is only one affiliation.) \address{% diff --git a/tex/chapters/abstract.tex b/tex/chapters/abstract.tex index 279f19e..368cb36 100644 --- a/tex/chapters/abstract.tex +++ b/tex/chapters/abstract.tex @@ -1,4 +1,20 @@ \abstract{ -Abstracttatatata +Within this work we present an updated version of our award-winning indoor localization system for smartphones. +The current position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. +Absolute positioning information is given by a comparison between recent Wi-Fi measurements of nearby access-points and signal strength predictions. +Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access-points, we use an optimization scheme based on reference measurements to estimate a corresponding Wi-Fi model. +To model the pedestrian's movement, which is constraint by walls and other obstacles, we propose a state transition based upon navigation meshes, modelling only the buildings walkable areas. +Continuous and smooth floor changes are enabled by using a simple activity recognition. +Our rapid computation scheme of the kernel density estimation allows to find an exact estimation of the pedestrian's current position. +We further tackle advanced problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures, leading to a more robust localization. +\newline +The goal of this work is to propose a fast to deploy and low-cost localization solution, that +provides reasonable results in a high variety of situations. +Therefore, we have chosen a very challenging test scenario. +All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. +The system is evaluated using 28 distinct measurement series on four different test walks, up to \SI{600}{\meter} length and \SI{10}{\minute} duration. +It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. +Our advanced filtering methods allow for a real fail-safe system, while the novel optimization scheme enables a setup-time of under \SI{120}{\minute} for the complete building. +%We are able to resolve sample impoverishment whenever it occurs and thus provide a real fail-safe system. +%finally compare the standard weighted-average estimator with our kernel density approach. } -