in progress.. related work

tex/chapters/introduction.tex

@@ -2,7 +2,7 @@
 
 Since the advent of smartphones, location aware apps and services are ubiquitous and have become a natural part of our lives. Whether driving a car, jogging or shopping in the streets, GNSS-based applications are making orientation easier, point the way and even track our fitness achievements. But as soon as we drive into an underground car park or visit a shopping mall, they perform poorly. That is because satellite signals are to weak to pass through obstacles like buildings' walls. Moreover, their accuracy is not sufficient for individual parking spaces or rooms. Therefore, many different solutions for localizing a moving object within buildings have been developed in recent years \cite{}. Especially the hard problem of pedestrian localization and navigation has lately attracted a lot of interest. 
 
-Most modern indoor localisation systems primarily use smartphones for determining the position of a pedestrian. Especially the phone's inertial measurement unit (IMU) as well as external information like Wi-Fi or Bluetooth are used for collecting the necessary data. Additionally, environmental knowledge is often incorporated by using floor maps. This combination of highly different sensor types is also known as sensor fusion. Here probabilistic methods like particle filters or Kalman filters or often used to approximate a probability distribution describing the uncertainties of the system. This procedure can be separated into two probabilistic models: The transition model represents the dynamics of the system and predicts the next accessible locations, while the evaluation model estimates a probability that the position also corresponds to the current sensor measurement.
+Most modern indoor localisation systems primarily use smartphones for determining the position of a pedestrian. Especially the phone's inertial measurement unit (IMU) as well as external information like Wi-Fi or Bluetooth are used for collecting the necessary data. Additionally, environmental knowledge is often incorporated by using floor maps. This combination of highly different sensor types is also known as sensor fusion. Here, probabilistic methods like particle filters or Kalman filters or often used to approximate a probability distribution describing the uncertainties of the system. This procedure can be separated into two probabilistic models: The transition model represents the dynamics of the system and predicts the next accessible locations, while the evaluation model estimates a probability that the position also corresponds to the current sensor measurement.
  %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 above mentioned sensors including the phone's barometer \cite{Ebner-15}. 
 
@@ -15,6 +15,8 @@ Secondly, the heading for modeling the pedestrian's walking behaviour is calcula
 
 The problem of localization can be simplified by assuming a person navigation. Such applications are used to navigate a pedestrian to a given target destination. So, based on this assumption the starting point, which is the current position of the pedestrian, as well as the destination are known beforehand. Regarding a graph-based transition model, one could suggest to calculate the shortest path between start and destination. However, this often leads to paths running very unnatural alongside walls. Additionally, the human walking behaviour is highly affected by visual distractions, comfort, disorientation and many other factors. Therefore, we present a novel method for pedestrian navigation by using XXX methods to achieve a preferably realistic path, areas near a wall are less likely to be chosen for the path then a door or a small hallway. ... probability map/graph ... 
 \commentByToni{Wissen ja noch nicht was wir hier genau nehmen, deswegen erstmal leer}
+to address the problem of walking on a corridor with higher probability ... a method for detecting doors and reducing the proabability of walking alongside walls will be presentend within this work...
+
 
 The work is structured as follows... 
 

tex/chapters/relatedwork.tex

@@ -1,27 +1,24 @@
 \section{Related Work}
 
-Like mentioned before, most state-of-the-art systems use recursive state estimators like Kalman filters and particle filters. They differ mainly by the sensors used, their probabilistic models and how the environmental information is incorporated. For example \cite{} recently presentend an approach based on ... . 
+Like mentioned before, most state-of-the-art systems use recursive state estimators like Kalman filters and particle filters. They differ mainly by the sensors used, their probabilistic models and how the environmental information is incorporated. For example \cite{Li2015} recently presented an approach combining methods of pedestrian dead reckoning (PDR), Wi-Fi fingerprinting and magnetic matching using a Kalman filter. Regardless of the good results, fingerprinting methods require an extensive offline calibration phase. Therefore, many other systems like \cite{Fang09} or \cite{Ebner-15} are using signal strength prediction models like the log-distance model or wall-attenuation-factor model. Additionally, the sensors noise is not always Gaussian or satisfies the central limit theorem, what makes the usage of Kalman filters problematic \cite{sarkka2013bayesian, Nurminen2014}. 
 
-and \cite{} are combining pedestrian dead reckoning (PDR), Wi-Fi and  as information source. Here, \cite{} uses a fingerprinting approach for wi-fi in contrast to the ... model of \cite{}. This shows, that sensor models differ in many ways and are a subject in itself. However, in regard of this work, we are not that interested in the different sensor representations but more in the state transition as well as incorporating environmental and navigational knowledge. A good discussion on different sensor models can be found in \cite{} or \cite{}. 
+All this shows, that sensor models differ in many ways and are a subject in itself. However, in regard of this work, we are not that interested in the different sensor representations but more in the state transition as well as incorporating environmental and navigational knowledge. A good discussion on different sensor models can be found in \cite{Yang2015}, \cite{Gu2009} or \cite{Khaleghi2013}. 
 
 A widely used and easy method for modelling the movement of a pedestrian, is the prediction of a new position by adding an approximated covered distance to the current position. In most cases, the heading serves as walking direction. If the connection line between the new and the old position intersects a wall, the probability for the new position is set to zero \cite{Woodman08-PLF, Blanchert09-IFF}. 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. This can result in a high computational complexity. 
 
-To avoid these disadvantages, from the outset, graph-based methods are becoming more and more popular. 
+These disadvantages can be avoided, from the outset, by using spatial models like indoor graphs. Regarding modelling approaches, two main classes are inferred: symbolic and geometric spatial models \cite{Afyouni2012}. Especially geometric spatial models (coordinate-based approaches) are very popular, since they integrate metric properties to provide highly accurate location and distance information. One of the most common environmental representations in indoor localization literature is the Voronoi diagram \cite{Liao2003}. It represents the topological skeleton of the building's floorplan as an irregular tessellation of space. 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. The choice probabilities of changing to the next link are proportional to the total link lengths. However, for highly accurate localisation and 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 from this structure. Similar to \cite{Ebner-15}, they provide a state transition model that selects and edge and a node from the graph according to a sampled distance and heading. 
 
-If the connection line between the new and the old position
+Nevertheless, most corridors 
 
-spatial models for indoor localization systems
-kann man unterscheiden: graph-based and random walk/non-graph based systeme. 
+...and walking into a rooms unwahrscheinlich.
+
+deshalb grided tessellation graph. blabalba for 2D environments. later for 3D .. 
+
+also hyprid version of both like presented in. they use blabal.. balab 
 
 
-random walk systeme
-
-graph systeme
-
-the work nearest to ours... 
-
-grided graph. blabalba for 2D environments. later for 3D ... 
 
+remove degrees of freedom from the map -> less particles
 
 \subsection{State Transition}
 

tex/egbib.bib

@@ -1830,13 +1830,22 @@ number={1},
 pages={414-454}, 
 }
 
-@inproceedings{Nurminen14-MMF,
- author = {Henri Nurminen and Mike Koivisto and Simo Ali-Löytty and Robert Pichè},
- title = {{Motion Model for Positioning with Graph-Based Indoor Map}},
- booktitle = {Indoor Positioning and Indoor Navigation (IPIN), International Conference on},
- year = {2014},
- pages = {1--10},
-} 
+@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)},
+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},
+keywords = {Atmospheric measurements,Computational modeling,Particle measurements,Position measurement,Proposals,TLL,Training data,Wireless LAN,graph,graph theory,graph-based indoor map,indoor navigation,indoor positioning,map-matching,motion model,particle filter,particle filtering (numerical methods),position measurement,radio links,real-data WLAN positioning test,total link length,training-free probabilistic pedestrian motion mode,wireless LAN},
+month = {oct},
+pages = {646--655},
+publisher = {IEEE},
+shorttitle = {Indoor Positioning and Indoor Navigation (IPIN), 2},
+title = {{Motion model for positioning with graph-based indoor map}},
+year = {2014}
+}
+
 
 @book{albrecht2013computer,
   title={Computer Algebra: Symbolic and Algebraic Computation},
@@ -2429,3 +2438,120 @@ Booktitle = {Indoor Positioning and Indoor Navigation (IPIN), International Conf
 Year = {2014},
 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)},
+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},
+keywords = {5th EvAAL indoor navigation competition,Fingerprint recognition,IEEE 802.11 Standard,Indoor navigation,Localisation System,MEMS sensors,Magnetometers,Position measurement,Sensors,WiFi fingerprinting,WiFi positioning,accelerometers,barometer,barometers,dead-reckoning,floor change detection,gyros,gyroscopes,indoor navigation,magnetic features,magnetic matching,magnetometers,micromechanical devices,position-tracking module,radionavigation,real-time indoor navigation,smart phones,smartphone,smartphone sensors,wireless LAN},
+mendeley-tags = {Localisation System},
+month = {oct},
+pages = {1--10},
+publisher = {IEEE},
+shorttitle = {Indoor Positioning and Indoor Navigation (IPIN), 2},
+title = {{Real-time indoor navigation using smartphone sensors}},
+year = {2015}
+}
+
+@article{Yang2015,
+author = {Yang, Zheng and Wu, Chenshu and Zhou, Zimu and Zhang, Xinglin and Wang, Xu and Liu, Yunhao},
+doi = {10.1145/2676430},
+file = {:home/toni/Documents/literatur/fusion16/Mobility Increases Localizability$\backslash$: A Survey on Wireless Indoor.pdf:pdf},
+issn = {03600300},
+journal = {ACM Computing Surveys},
+keywords = {Mobility,smartphones,wireless indoor localization},
+month = {apr},
+number = {3},
+pages = {1--34},
+publisher = {ACM},
+title = {{Mobility Increases Localizability: A Survey on Wireless Indoor Localization using Inertial Sensors}},
+volume = {47},
+year = {2015}
+}
+
+@article{Gu2009,
+abstract = {Recently, indoor positioning systems (IPSs) have been designed to provide location information of persons and devices. The position information enables location-based protocols for user applications. Personal networks (PNs) are designed to meet the users' needs and interconnect users' devices equipped with different communications technologies in various places to form one network. Location-aware services need to be developed in PNs to offer flexible and adaptive personal services and improve the quality of lives. This paper gives a comprehensive survey of numerous IPSs, which include both commercial products and research-oriented solutions. Evaluation criteria are proposed for assessing these systems, namely security and privacy, cost, performance, robustness, complexity, user preferences, commercial availability, and limitations. We compare the existing IPSs and outline the trade-offs among these systems from the viewpoint of a user in a PN.},
+author = {Gu, Yanying and Lo, Anthony and Niemegeers, Ignas},
+doi = {10.1109/SURV.2009.090103},
+file = {:home/toni/Documents/literatur/fusion16/A Survey of Indoor Positioning Systems for.pdf:pdf},
+isbn = {1553-877X},
+issn = {1553877X},
+journal = {IEEE Communications Surveys and Tutorials},
+keywords = {Indoor positioning systems,Location techniques,Personal networks},
+number = {1},
+pages = {13--32},
+shorttitle = {Communications Surveys {\&} Tutorials, IEEE},
+title = {{A survey of indoor positioning systems for wireless personal networks}},
+volume = {11},
+year = {2009}
+}
+
+@article{Khaleghi2013,
+abstract = {There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.},
+author = {Khaleghi, Bahador and Khamis, Alaa and Karray, Fakhreddine O. and Razavi, Saiedeh N.},
+doi = {10.1016/j.inffus.2011.08.001},
+file = {:home/toni/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Khaleghi et al. - 2013 - Multisensor data fusion A review of the state-of-the-art.pdf:pdf},
+issn = {15662535},
+journal = {Information Fusion},
+keywords = {Fusion methodologies,Multisensor data fusion,Taxonomy},
+month = {jan},
+number = {1},
+pages = {28--44},
+title = {{Multisensor data fusion: A review of the state-of-the-art}},
+volume = {14},
+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 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)},
+doi = {10.1109/IROS.2003.1250715},
+file = {:home/toni/Documents/literatur/fusion16/Voronoi .pdf:pdf},
+isbn = {0-7803-7860-1},
+keywords = {Computer science,Humans,Indoor environments,Particle filters,Pattern recognition,Robot sensing systems,Robustness,State-space methods,Ultrasonic imaging,Voronoi graph,Voronoi tracking,Working environment noise,array signal processing,computational geometry,high-level behavior recognition,human motion,indoor environments,infrared badge system,mobile robots,motion estimation,noisy sensor data,particle filters,robot,tracking,tracking moving objects,ultrasound badge systems,unsupervised learning techniques},
+pages = {723--728},
+publisher = {IEEE},
+shorttitle = {Intelligent Robots and Systems, 2003. (IROS 2003).},
+title = {{Voronoi tracking: location estimation using sparse and noisy sensor data}},
+volume = {1},
+year = {2003}
+}
+
+
+@article{Afyouni2012,
+abstract = {This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, considering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation purposes, and by taking into account the context dimensions.},
+author = {Afyouni, Imad and Ray, Cyril and Claramunt, Christophe},
+doi = {10.5311/JOSIS.2012.4.73},
+file = {:home/toni/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Afyouni, Ray, Claramunt - 2012 - Spatial models for context-aware indoor navigation systems A survey.pdf:pdf},
+issn = {1948-660X},
+journal = {Journal of Spatial Information Science},
+keywords = {context-awareness,indoor spatial data models,location-dependent queries,navigation systems and wayfinding,qualitative spatial representation,quantitative spatial representation},
+language = {en},
+month = {jun},
+number = {4},
+pages = {85----123},
+title = {{Spatial models for context-aware indoor navigation systems: A survey}},
+volume = {1},
+year = {2012}
+}
+
+@inproceedings{Hilsenbeck2014,
+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, New York, 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},
+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},
+keywords = {Graph-based Sensor Fusion,Indoor Navigation,Indoor Positioning,Location-based Services,Mobile Computing,Particle Filter,Ubiquitous Localization},
+month = {sep},
+pages = {147--158},
+publisher = {ACM Press},
+title = {{Graph-based Data Fusion of Pedometer and WiFi Measurements for Mobile Indoor Positioning}},
+year = {2014}
+}
+