Towards a robust smartphone-based 2,5D pedestrian localization

Ehrlich, Catia Real; Blankenbach, Jörg; Sieprath, Arnd

doi:10.1109/ipin.2016.7743597

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…The work presented in [ 40 ] shows some plots with measured probability and cumulative distribution instead of providing simple evaluation numbers. Estimated versus real trajectory are the results shown in [ 34 ].…”

Section: Diversity Problem Reviewmentioning

confidence: 99%

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

Potortì

Park

Ruiz

et al. 2017

Sensors

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In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems.

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Section: Diversity Problem Reviewmentioning

confidence: 99%

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

Potortì

Park

Ruiz

et al. 2017

Sensors

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“…Simple 2D setups e.g. describe each floor with lines that can be used for intersection tests, to determine impossible walks [EBS16]. This, however, is not suitable for most buildings, as they consist of multiple stories.…”

Section: State Of the Artmentioning

confidence: 99%

“…Alternatively to complex fingerprinting, Ehrlich et al [EBS16] suggest installing a few special coil-transmitters within the building, each creating a unique magnetic signature within a confined area. Those signatures can be measured by the magnetometer, and compared against a database of known transmitter signatures.…”

Section: Ecompassmentioning

confidence: 99%

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Smartphone-Based 3D Indoor Localization and Navigation

Ebner¹

2021

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During my master's thesis, which covered the essentials of Wi-Fi location estimation, I came in contact with the concepts of indoor localization, and multi sensor fusion. Having attracted my interest, I hereafter joined the Faculty of Computer Science and Business Information Systems at the University of Applied Sciences Würzburg-Schweinfurt as a research assistant. During that time I conducted the majority of my research on indoor localization and navigation, which finally lead to writing this work.I would like to thank my colleagues during that time, but especially my local doctoral adviser Prof. Dr. Frank Deinzer. Not only did he guide me towards earning a doctors degree, he also provided extensive feedback to all publications, always ensuring utmost scientific quality. I would also like to thank my external doctoral adviser and first referee Prof. Dr. habil. Marcin Grzegorzek for his trust, all provided feedback, and the warm welcome within his research group. I also owe gratitude to my co-workers Lukas, Toni, and Markus. Without hours of mutual feedback, technical input, paired programming, data acquisition, and networking, this work would not have been possible. Likewise, I also wish to thank Prof. Dr. Karsten Huffstadt and Prof. Dr. Arndt Balzer for helping with funding my initial employment.In order to develop a generic indoor localization and navigation solution, the presented system was deployed to and tested within several unique buildings. For this, special thanks go to Dr. Hellmuth Möhring from the RothenburgMuseum in Rothenburg ob der Tauber, Prof. Dr. Günther Moosbauer from the Gäubodenmuseum in Straubing, Angelika Schreiber from the Hutmuseum in Lindenberg, the Landesstelle für die nichtstaatlichen Museen in Bayern, and the Sparkassenstiftung. By providing access and enabling temporal hardware installations they allowed gathering data within a range of unique and representative public buildings, enhancing the quality of the developed system, and the experiments provided within this work.Thanks also go to Markus, Christin, Alexander, and Maximilian, not only for spending numerous hours of their spare time with proof reading and feedback, but also for all emotional support while conducting my research and writing this work. The same goes to my parents, for always supporting me, and my education.To Katharina, who has been a true friend, and will never be forgotten.

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“…In contrast, Bahl and Padmanabhan (2000), as well as Li et al (2006), present an indoor positioning system based on WLAN. Further systems can be found in Blankenbach (2010), Michalson, Navalekar, and Parikh (2009) or Zwirello et al (2012) using UWB, infrared (Want et al 1992), or ultrasound (Priyantha, Chakraborty, and Balakrishnan 2000). Despite intensive research on Real-Time Location Systems (RTLS) for indoor areas, no technology or method has been established as a standard.…”

Section: State Of the Artmentioning

confidence: 99%

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

Ehrlich

Blankenbach

2019

Geo-spatial Information Science

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The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today's devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multisensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.

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Towards a robust smartphone-based 2,5D pedestrian localization

Cited by 9 publications

References 14 publications

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

Smartphone-Based 3D Indoor Localization and Navigation

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

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