@@ -13,8 +13,7 @@ prediction models e.g. incorporating wall information.
As seen, multimodal distributions lead to faulty position estimations and therefore rising errors.
One possible method to resolve this issue would be a more suiting location estimation technique.
Another promising way is smoothing.
By deploying a fixed-lag smoother the system would still be perceived as real-time application,
but is able to calculate the (delayed) estimation using future measurements up to the latest timestep.
By deploying a fixed-lag smoother the system would still be perceived as real-time application, but is able to calculate the (delayed) estimation using future measurements up to the latest timestep.
Since the advent of smartphones, location aware apps and services are ubiquitous and have become a natural part of our everyday life.
Whether driving a car, jogging or shopping in the streets, GNSS-based applications simplify orientation,
guide the way and even track our fitness achievements. But as soon as we drive into an underground car park or visit a shopping mall, most of them do not work at all.
That is because satellite signals are too weak to pass through obstacles like ceilings.
Moreover, their accuracy is not sufficient for individual parking spaces or office rooms.
%Whether driving a car, jogging or shopping in the streets,
GNSS-based applications simplify orientation,
guide the way and even track our fitness achievements.
%But as soon as we drive into an underground car park or visit a shopping mall, most of them do not work at all.
%That is because satellite signals are too weak to pass through obstacles like ceilings.
However, satellite signals are too weak to pass through obstacles like ceilings and their accuracy is not sufficient for most indoor tasks.
%Moreover, their accuracy is not sufficient for individual parking spaces or office rooms.
Therefore, many different solutions for localising a moving object within buildings have been developed in
the most recent years \cite{Ebner-15, Yang2015, Khaleghi2013, Fang09, Nurminen2014}.
Especially the hard problem of pedestrian localisation and navigation has lately attracted a lot of interest.
Especially the hard problem of pedestrian navigation has lately attracted a lot of interest.
Most modern indoor localisation systems primarily use smartphones to determine the position of a pedestrian.
Especially the phone's inertial measurement unit (IMU) as well as external information like Wi-Fi or Bluetooth
@@ -15,16 +18,18 @@ are used to collect the necessary data.
Additionally, environmental knowledge is often incorporated e.g. by using floormaps.
This combination of highly different sensor types is also known as sensor fusion.
Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability distribution describing the pedestrian's possible whereabouts.
Here, probabilistic methods like particle- or Kalman filters are often used to approximate a probability density describing the pedestrian's possible whereabouts.
This procedure can be separated into two probabilistic models:
The transition model, which represents the dynamics of the pedestrian and predicts his next accessible locations, and the evaluation model, which estimates the probability for the position also corresponding to
recent sensor measurements.
%Therefore, the most accurate position is represented by a peak of the probability distribution.
In our previous work we were able to present such a localisation system based on all the sensors mentioned above, including the phone's barometer \cite{Ebner-15}.
In pedestrian navigation, the human movement is subject to the characteristics of walking speed and -direction.
Additionally, environmental restrictions need to be considered as well, for example, walking through walls is impossible.
Therefore, incorporating environmental knowledge is a necessary and gainful step.
%In pedestrian navigation, the human movement is subject to the characteristics of walking speed and -direction.
%Additionally, environmental restrictions need to be considered as well, for example, walking through walls is impossible.
%Therefore, incorporating environmental knowledge is a necessary and gainful step.
Incorporating environmental knowledge is a necessary and gainful step.
For example walking through walls is impossible.
Like other systems, we are using a graph-based approach to sample only valid movements.
The unique feature of our approach is the way how human movement is modelled.
This is done by using random walks on a graph, which are based on the heading of the pedestrian.
@@ -32,7 +37,7 @@ Despite very good results, the system presented in \cite{Ebner-15} suffers from
First, the transition model of our previous approach uses discrete floor-changes.
Although the overall system provides viable results, it does not resemble real-world floor changes.
Especially the barometric sensor is affected due to its continuous pressure measurements.
Especially the barometer is affected due to its continuous pressure measurements.
The discrete model prevents the barometer's full potential.
It could further be shown that a correct estimation strongly depends on the quality of $z$-transitions.
To address this problem we extended the graph by adding realistic stairs, allowing a step-wise transition in the $z$-direction.
@@ -46,7 +51,7 @@ During the random walk, matching edges are sampled according to their deviation
To improve the complex problem of localising a person indoors, prior knowledge given by a navigation system can be used.
Such applications are used to navigate a user to his desired destination.
This limits the unpredictability of human movement to a certain degree.
So, based on this assumption, the destination is known beforehand and the starting point is the pedestrian's currently estimated position.
So, based on this assumption, the destination is known beforehand and the starting point is the user's currently estimated position.
Regarding a graph-based transition model, one could suggest to use the shortest route between start and destination as the user's most-likely-to-walk path.
By incorporating this prior knowledge into the state transition step, a new state can be sampled in a more targeted manner.
However, for regularly tessellated (grid) graphs, as used in \cite{Ebner-15}, this would lead to unnatural paths e.g.
@@ -58,7 +63,7 @@ Since areas near walls are less likely to be chosen for walking, a probabilistic
This allows a variety of options for integrating additional knowledge about the environment and enables us to address another problem:
Entering or leaving rooms is very unlikely as only a few nodes are representing doors and allow doing so.
This can be tackled by making such areas more likely.
Therefore, a novel approach for detecting doors using the inverted graph and a principal component analysis (PCA) \cite{Hotelling1933} is presented within this work.
Therefore, a novel approach for detecting doors using the inverted graph is presented within this work.
%\commentByFrank{auch hier vlt das inverted erstmal noch weg lassen wegen platz}
Finally, it is now possible to calculate more natural and realistic paths using the weighted graph.
@@ -11,7 +11,7 @@ use signal strength prediction models like the log-distance or wall-attenuation-
Additionally, the sensors noise is not always Gaussian or satisfies the central limit theorem. Using
Kalman filters is therefore problematic \cite{sarkka2013bayesian, Nurminen2014}.
All this shows, that sensor models differ in many ways and are a subject in itself.
A good discussion on different sensor models can be found in \cite{Yang2015}, \cite{Gu2009} or \cite{Khaleghi2013}.
A good discussion on different sensor models can be found in \cite{Yang2015} or \cite{Khaleghi2013}.
However, within this work, we use simple models, configured using a handful of empirically chosen parameters and
address their inaccuracies by harnessing prior information like the pedestrian's desired destination. Therefore,
@@ -21,7 +21,7 @@ on the state transition and how to incorporate environmental and navigational kn
A widely used and easy method for modelling the movement of a pedestrian, is the prediction of a new position
using both, a walking direction and a to-be-walked distance, starting from the previous position.
If the line-of-sight between the new and the old position intersects a wall, the probability for this
transition is set to zero \cite{Woodman08-PLF, Blanchert09-IFF, Koeping14-ILU}.
transition is set to zero \cite{Blanchert09-IFF, Koeping14-ILU}.
However, as \cite{Nurminen13-PSI} already stated, it "gives more probability to a short step".
An additional drawback of these approaches is that for every transition an intersection-test
must be executed and thus often yields a high computational complexity.
@@ -34,15 +34,12 @@ It represents the topological skeleton of the building's floorplan as an irregul
This drastically removes degrees of freedom from the map, and results in a low complexity.
In the work of \cite{Nurminen2014} a Voronoi diagram is used to approximate the human movement.
It is assumed that the pedestrian can be anywhere on the topological links.
It is assumed that the user can be anywhere on the topological links.
The probabilities of changing to the next link are proportional to the total link lengths.
However, for highly accurate localisation in large-scale buildings, this network of one-dimensional
curves is not suitable\cite{Afyouni2012}.
Therefore, \cite{Hilsenbeck2014} searches for large open spaces (e.g. a lobby) and extends the Voronoi diagram
by adding those two-dimensional areas.
The final graph is then created by sampling nodes in regular intervals across the links and filling up the open
spaces in a tessellated manner. Similar to \cite{Ebner-15}, they provide a state transition model that selects
an edge and a node from the graph according to a sampled distance and heading.
However, for accurate localisation in large-scale buildings, this network of one-dimensional curves is not suitable \cite{Afyouni2012}.
Therefore,\cite{Hilsenbeck2014} searches for large open spaces (e.g. a lobby) and extends the Voronoi diagram by adding those two-dimensional areas.
The final graph is then created by sampling nodes in regular intervals across the links and filling up the open spaces in a tessellated manner.
Similar to \cite{Ebner-15}, they provide a transition model that selects an edge and a node from the graph according to a sampled distance and heading.
Nevertheless, most corridors are still represented by just one topological link.
While the complexity is reduced, it does not allow arbitrary movements and leads to suboptimal trajectories.
@@ -74,7 +71,7 @@ An additional smoothing procedure is performed to make the path more natural.
They are considering foot span, body dimensions and obstacle dimensions when determining whether an obstacle is surmountable.
However, many of this information is difficult to ascertain in real-time or imply additional effort in real-world environments.
Therefore, more realistic simulation models, mainly for evacuation simulation, are just using a simple shortest path on regularly
tessellated graphs \cite{Sun2011, tan2014agent}. A more costly, yet promising approach is shown by \cite{Brogan2003}. They use a
tessellated graphs \cite{tan2014agent}. A more costly, yet promising approach is shown by \cite{Brogan2003}. They use a
data set of previously recorded walks to create a model of realistic human walking paths.
Finally, it seems that currently none of the localisation system approaches are using realistic walking paths as additional
booktitle={Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2013 10th International Conference on},
booktitle={Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2013 10th Int. Conf. on},
title={Indoor WIFI localization on mobile devices},
title={Fusion of barometric sensors, WLAN signals and building information for 3-D indoor/campus localization},
author={Wang, Hui and Lenz, Henning and Szabo, Andrei and Hanebeck, Uwe D and Bamberger, Joachim},
booktitle={Proceedings of International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), S},
booktitle={Proc. of Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), S},
pages={426--432},
year={2006},
}
@inproceedings{Ebner-15,
author={Ebner, Frank and Fetzer, Toni and K{\"o}ping, Lukas and Grzegorzek, Marcin and Deinzer, Frank},
booktitle={Indoor Positioning and Indoor Navigation (IPIN), International Conference on},
booktitle={Indoor Positioning and Indoor Navigation (IPIN), Int. Conf. on},
title={{Multi Sensor 3D Indoor Localisation}},
year={2015},
IGNOREmonth={October},
@@ -1804,7 +1804,7 @@ pages={95-97},
@inproceedings{Macvean12-IAS,
author={Macvean, Andrew and Robertson, Judy},
title={{iFitQuest: A School Based Study of a Mobile Location-aware Exergame for Adolescents}},
booktitle={Proceedings of the 14th International Conference on Human-computer Interaction with Mobile Devices and Services},
booktitle={Proc. of the 14th Int. Conf. on Human-computer Interaction with Mobile Devices and Services},
series={MobileHCI '12},
year={2012},
pages={359--368},
@@ -1813,7 +1813,7 @@ pages={95-97},
@inproceedings{Kaminskas13-LAM,
author={Kaminskas, Marius and Ricci, Francesco and Schedl, Markus},
title={{Location-aware Music Recommendation Using Auto-tagging and Hybrid Matching}},
booktitle={Proceedings of the 7th ACM Conference on Recommender Systems},
booktitle={Proc. of the 7th ACM Conf. on Recommender Systems},
series={RecSys '13},
year={2013},
pages={17--24},
@@ -1833,7 +1833,7 @@ pages={414-454},
@inproceedings{Nurminen2014,
abstract={This article presents a training-free probabilistic pedestrian motion model that uses indoor map information represented as a set of links that are connected by nodes. This kind of structure can be modelled as a graph. In the proposed model, as a position estimate reaches a link end, the choice probabilities of the next link are proportional to the total link lengths (TLL), the total lengths of the subgraphs accessible by choosing the considered link alternative. The TLLs can be computed off-line using only the graph, and they can be updated if training data are available. A particle filter in which all the particles move on the links following the TLL-based motion model is formulated. The TLL-based motion model has advantageous theoretical properties compared to the conventional models. Furthermore, the real-data WLAN positioning tests show that the positioning accuracy of the algorithm is similar or in many cases better than that of the conventional algorithms. The TLL-based model is found to be advantageous especially if position measurements are used infrequently, with 10-second or more time intervals.},
author={Nurminen, Henri and Koivisto, Mike and Ali-Loytty, Simo and Piche, Robert},
booktitle={2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
booktitle={Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN)},
doi={10.1109/IPIN.2014.7275539},
file={:home/toni/Documents/literatur/fusion16/Motion model for positioning with graph-based indoor map.pdf:pdf},
isbn={978-1-4673-8054-6},
@@ -1901,7 +1901,7 @@ year = {2014}
@article{ghahramani2001introduction,
title={An Introduction to Hidden Markov Models and Bayesian Networks},
author={Ghahramani, Zoubin},
journal={International Journal of Pattern Recognition and Artificial Intelligence},
journal={Int. Journal of Pattern Recognition and Artificial Intelligence},
volume={15},
number={01},
pages={9--42},
@@ -1912,7 +1912,7 @@ year = {2014}
@article{rabiner1989tutorial,
title={A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition},
author={Rabiner, Lawrence R},
journal={Proceedings of the IEEE},
journal={Proc. of the IEEE},
volume={77},
number={2},
pages={257--286},
@@ -1923,7 +1923,7 @@ year = {2014}
@inproceedings{wang2013collapsed,
title={Collapsed variational Bayesian Inference for Hidden Markov Models},
author={Wang, Pengyu and Blunsom, Phil},
booktitle={Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics},
booktitle={Proc. of the Sixteenth Int. Conf. on Artificial Intelligence and Statistics},
pages={599--607},
year={2013}
}
@@ -1931,7 +1931,7 @@ year = {2014}
@article{baum1966statistical,
title={Statistical Inference for Probabilistic Functions of Finite State Markov Chains},
author={Baum, Leonard E and Petrie, Ted},
journal={The Annals of Mathematical Statistics},
journal={The Ann. of Mathematical Statistics},
pages={1554--1563},
year={1966},
publisher={JSTOR}
@@ -2012,7 +2012,7 @@ year = {2014}
doi={10.1098/rspa.1946.0056},
publisher={The Royal Society},
issn={0080-4630},
journal={Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences}
journal={Proc. of the Royal Society of London A: Mathematical, Physical and Engineering Sciences}
}
@inproceedings{julier1997new,
@@ -2021,13 +2021,13 @@ year = {2014}
booktitle={AeroSense'97},
pages={182--193},
year={1997},
organization={International Society for Optics and Photonics}
organization={Int. Society for Optics and Photonics}
}
@article{rosenblatt1956central,
title={A Central Limit Theorem and a Strong Mixing Condition},
author={Rosenblatt, Murray},
journal={Proceedings of the National Academy of Sciences of the United States of America},
journal={Proc. of the National Academy of Sciences of the United States of America},
volume={42},
number={1},
pages={43},
@@ -2128,7 +2128,7 @@ language={English}
@inproceedings{douc2005comparison,
title={Comparison of Resampling Schemes for Particle Filtering},
author={Douc, Randal and Capp{\'e}, Olivier},
booktitle={Image and Signal Processing and Analysis, 2005. ISPA 2005. Proceedings of the 4th International Symposium on},
booktitle={Image and Signal Processing and Analysis, 2005. ISPA 2005. Proc. of the 4th Int. Symp. on},
pages={64--69},
year={2005},
organization={IEEE}
@@ -2154,7 +2154,7 @@ language={English}
@ARTICLE{Gordon93,
author={Gordon, N.J. and Salmond, D.J. and Smith, A.F.M.},
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@@ -2194,7 +2194,7 @@ IGNOREmonth={Apr},
@article{kunsch2005recursive,
title={Recursive Monte Carlo Filters: Algorithms and Theoretical Analysis},
author={K{\"u}nsch, Hans R},
journal={Annals of Statistics},
journal={Ann. of Statistics},
pages={1983--2021},
year={2005},
publisher={JSTOR}
@@ -2210,7 +2210,7 @@ IGNOREmonth={Apr},
@article{cappe2007overview,
title={An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo},
author={Capp{\'e}, Olivier and Godsill, Simon J and Moulines, Eric},
journal={Proceedings of the IEEE},
journal={Proc. of the IEEE},
volume={95},
number={5},
pages={899--924},
@@ -2240,7 +2240,7 @@ IGNOREmonth={Apr},
@inproceedings{wan2000unscented,
title={The unscented Kalman filter for nonlinear estimation},
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booktitle={Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000},
booktitle={Adaptive Systems for Signal Processing, Communications, and Control Symp. 2000. AS-SPCC. The IEEE 2000},
pages={153--158},
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@@ -2249,7 +2249,7 @@ IGNOREmonth={Apr},
@inproceedings{Muller:2003:PFS,
author={M\"{u}ller, Matthias and Charypar, David and Gross, Markus},
title={Particle-based Fluid Simulation for Interactive Applications},
booktitle={Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation},
booktitle={Proc. of the 2003 ACM SIGGRAPH/Eurographics Symp. on Computer Animation},
series={SCA '03},
year={2003},
isbn={1-58113-659-5},
@@ -2274,7 +2274,7 @@ IGNOREmonth={Apr},
@inproceedings{klaas2006fast,
title={Fast Particle Smoothing: If I had a Million Particles},
author={Klaas, Mike and Briers, Mark and De Freitas, Nando and Doucet, Arnaud and Maskell, Simon and Lang, Dustin},
booktitle={Proceedings of the 23rd international conference on Machine learning},
booktitle={Proc. of the 23rd Int. Conf. on Machine learning},
pages={481--488},
year={2006},
organization={ACM}
@@ -2294,7 +2294,7 @@ IGNOREmonth={Apr},
@inproceedings{achtelik2009visual,
title={Visual Tracking and Control of a Quadcopter using a Stereo Camera System and Inertial Sensors},
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booktitle={Mechatronics and Automation, 2009. ICMA 2009. International Conference on},
booktitle={Mechatronics and Automation, 2009. ICMA 2009. Int. Conf. on},
pages={2863--2869},
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@@ -2324,7 +2324,7 @@ IGNOREmonth={Apr},
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title={Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks},
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pages={176--183},
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@@ -2333,7 +2333,7 @@ IGNOREmonth={Apr},
@article{briers2010smoothing,
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author={Briers, Mark and Doucet, Arnaud and Maskell, Simon},
journal={Annals of the Institute of Statistical Mathematics},
journal={Ann. of the Institute of Statistical Mathematics},
volume={62},
number={1},
pages={61--89},
@@ -2385,7 +2385,7 @@ IGNOREmonth={Apr},
@inproceedings{Robertson:2009:SLM,
author={Robertson, Patrick and Angermann, Michael and Krach, Bernhard},
title={Simultaneous Localization and Mapping for Pedestrians Using Only Foot-mounted Inertial Sensors},
booktitle={Proceedings of the 11th International Conference on Ubiquitous Computing},
booktitle={Proc. of the 11th Int. Conf. on Ubiquitous Computing},
author={Jensen, Christian S. and Hua Lu and Bin Yang},
booktitle={Mobile Data Management: Systems, Services and Middleware, 2009. MDM '09. Tenth International Conference on},
booktitle={Mobile Data Management: Systems, Services and Middleware, 2009. MDM '09. Tenth Int. Conf. on},
title={Graph Model Based Indoor Tracking},
year={2009},
pages={122-131},
@@ -2431,10 +2431,10 @@ pages={129-174},
language={English}
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@InProceedings{werner2014homotopy,
@inproceedings{werner2014homotopy,
Title={Homotopy and Alternative Routes in Indoor Navigation Scenarios},
Author={Martin Werner and Sebastian Feld},
Booktitle={Indoor Positioning and Indoor Navigation (IPIN), International Conference on},
Booktitle={Indoor Positioning and Indoor Navigation (IPIN), Int. Conf. on},
Year={2014},
Pages={1-9},
}
@@ -2442,7 +2442,7 @@ Pages = {1-9},
@inproceedings{Li2015,
abstract={This paper presents an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as MEMS sensors (i.e., gyros, accelerometers, magnetometers, and a barometer), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such dead-reckoning, WiFi positioning, and magnetic matching. DR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. Meanwhile, a barometer is used to detect floor changes, so as to switch floors and the WiFi and magnetic databases. This algorithm was tested during the 5th EvAAL indoor navigation competition. Position errors on three quarters (75 {\%}) of test points (totally 62 test points were selected to evaluate the algorithm) were under 6.6 m.},
author={Li, You and Zhang, Peng and Niu, Xiaoji and Zhuang, Yuan and Lan, Haiyu and El-Sheimy, Naser},
booktitle={2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
booktitle={Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN)},
doi={10.1109/IPIN.2015.7346966},
file={:home/toni/Documents/literatur/fusion16/Real-time indoor navigation using smartphone sensors.pdf:pdf},
isbn={978-1-4673-8402-5},
@@ -2508,7 +2508,7 @@ year = {2013}
@inproceedings{Liao2003,
abstract={ Tracking the activity of people in indoor environments has gained considerable attention in the robotics community over the last years. Most of the existing approaches are based on sensors, which allow to accurately determining the locations of people but do not provide means to distinguish between different persons. In this paper we propose a novel approach to tracking moving objects and their identity using noisy, sparse information collected by id-sensors such as infrared and ultrasound badge systems. The key idea of our approach is to use particle filters to estimate the locations of people on the Voronoi graph of the environment. By restricting particles to a graph, we make use of the inherent structure of indoor environments. The approach has two key advantages. First, it is by far more efficient and robust than unconstrained particle filters. Second, the Voronoi graph provides a natural discretization of human motion, which allows us to apply unsupervised learning techniques to derive typical motion patterns of the people in the environment. Experiments using a robot to collect ground-truth data indicate the superior performance of Voronoi tracking. Furthermore, we demonstrate that EM-based learning of behavior patterns increases the tracking performance and provides valuable information for high-level behavior recognition.},
author={Liao, Lin and Fox, D. and Hightower, J. and Kautz, H. and Schulz, D.},
booktitle={Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)},
booktitle={Proc. 2003 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2003)},
abstract={We propose a graph-based, low-complexity sensor fusion approach for ubiquitous pedestrian indoor positioning using mobile devices. We employ our fusion technique to combine relative motion information based on step detection with WiFi signal strength measurements. The method is based on the well-known particle filter methodology. In contrast to previous work, we provide a probabilistic model for location estimation that is formulated directly on a fully discretized, graph-based representation of the indoor environment. We generate this graph by adaptive quantization of the indoor space, removing irrelevant degrees of freedom from the estimation problem. We evaluate the proposed method in two realistic indoor environments using real data collected from smartphones. In total, our dataset spans about 20 kilometers in distance walked and includes 13 users and four different mobile device types. Our results demonstrate that the filter requires an order of magnitude less particles than state-of-the-art approaches while maintaining an accuracy of a few meters. The proposed low-complexity solution not only enables indoor positioning on less powerful mobile devices, but also saves much-needed resources for location-based end-user applications which run on top of a localization service.},
address={New York, NY, USA},
author={Hilsenbeck, Sebastian and Bobkov, Dmytro and Schroth, Georg and Huitl, Robert and Steinbach, Eckehard},
booktitle={Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '14 Adjunct},
booktitle={Proc. of the 2014 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing - UbiComp '14 Adjunct},
doi={10.1145/2632048.2636079},
file={:home/toni/Documents/literatur/fusion16/Graph-based Data Fusion of Pedometer and WiFi Measurements for Mobile Indoor Positioning.pdf:pdf},
isbn={9781450329682},
@@ -2649,7 +2649,7 @@ year = {2000}
@inproceedings{tan2014agent,
title={Agent-based simulation of building evacuation using a grid graph-based model},
author={Tan, Lu and Lin, Hui and Hu, Mingyuan and Che, Weitao},
booktitle={IOP Conference Series: Earth and Environmental Science},
booktitle={IOP Conf. Series: Earth and Environmental Science},
volume={18},
number={1},
year={2014},
@@ -2659,7 +2659,7 @@ year = {2000}
@inproceedings{Sun2011,
abstract={At present, application of GIS is in a process of transition from macro space to micro space, such as indoor space, a kind of micro environment that has a smaller scale than outdoor space. There have been some applications for indoor space, covering issues like path finding, emergency planning, object tracking, etc. Behind these applications, indoor spatial models are needed to illustrate how built environments are spatially represented. Although some modeling approaches have been proposed, most of them focus only on either structural or topological properties. In view of this problem, recently a grid graph-based modeling approach considering a built environment as a continuous framework is presented, which is able to combine both geometrical and structural properties. In this paper, we employ this approach to implement route analysis based on a hotel floor plan. The result might be applied to the planning for evacuation routes.},
author={Sun, Jing and Li, Xiang},
booktitle={Proceedings - 2011 19th International Conference on Geoinformatics, Geoinformatics 2011},
booktitle={Proc. - 2011 19th Int. Conf. on Geoinformatics, Geoinformatics 2011},
title={{Indoor Evacuation Routes Planning with a Grid Graph-based Model}},
year={2011}
}
@@ -2675,7 +2675,7 @@ year = {2011}
@inproceedings{Brogan2003,
abstract={Pedestrian navigation is a complex function of human dynamics, a desired destination, and the presence of obstacles. People cannot stop and start instantaneously and their turning abilities are influenced by kinematic and dynamical constraints. A realistic model of human walking paths is an important development for entertainment applications and many classes of simulations. We present a novel behavioral model of path planning that extends previous models through its significant use of pedestrian performance statistics that were obtained during a suite of experiments. We develop an original interpretation of quantitative metrics for measuring a model’s accuracy, and use it to compare our path planning approach to a popular contemporary method. Results indicate that this new path planning model better fits natural human behavior than previous models.},
author={Brogan, D. C. and Johnson, N. L.},
booktitle={Proceedings - IEEE Workshop on Program Comprehension},
booktitle={Proc. - IEEE Workshop on Program Comprehension},
doi={10.1109/CASA.2003.1199309},
file={:home/toni/Documents/literatur/Realistic human walking paths.pdf:pdf},
isbn={0769519342},
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