Exploring the limits of PDR-based indoor localisation systems under realistic conditions

Jackermeier, Robert; Ludwig, Bernd

doi:10.1080/17489725.2018.1541330

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…The location of these APs is not a prerequisite for positioning, which eliminates the need to model complex indoor signal propagation [11]. Moreover, fingerprinting systems are immune to accumulated positioning errors caused by Inertial Measurement Unit (IMU) drifts [12].…”

Section: A the Fingerprinting Approach To Indoor Positioningmentioning

confidence: 99%

Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review

Alhomayani,

Mahoor

2022

Preprint

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Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.

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Section: A the Fingerprinting Approach To Indoor Positioningmentioning

confidence: 99%

Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review

Alhomayani,

Mahoor

2022

Preprint

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“…Issues in multi-story buildings are the focus of Bhargava et al [ 18 ]. In contrast, pedestrian dead reckoning via a smartphone application is the method studied by Jackermeier and Ludwig [ 92 ]. A low-cost indoor navigation system based on maps and conventional smartphone sensors [ 22 ] promises to allow for an easy way to make public indoor spaces location aware.…”

Section: The Value Of Location Datamentioning

confidence: 99%

Location-aware systems or location-based services: a survey with applications to CoViD-19 contact tracking

Schmidtke¹

2020

J Reliable Intell Environ

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With the CoViD-19 pandemic, location awareness technologies have seen renewed interests due to the numerous contact tracking mobile application variants developed, deployed, and discussed. For some, location-aware applications are primarily a producer of geospatial Big Data required for vital geospatial analysis and visualization of the spread of the disease in a state of emergency. For others, comprehensive tracking of citizens constitutes a dangerous violation of fundamental rights. Commercial web-based location-aware applications both collect data and—through spatial analysis and connection to services—provide value to users. This value is what motivates users to share increasingly private and comprehensive data. The willingness of users to share data in return for services has been a key concern with web-based variants of the technology since the beginning. With a focus on two privacy preserving CoViD-19 contact tracking applications, this survey walks through the key steps of developing a privacy preserving context-aware application: from types of applications and business models, through architectures and privacy strategies, to representations.

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“…In our earlier study (Jackermeier and Ludwig, 2018), test persons were not required to interact at all; in fact, the ex-Advances in Cartography and GIScience of the International Cartographic Association, 2, 2019. 15th International Conference on Location Based Services, 11-13 November 2019, Vienna, Austria.…”

Section: Device Orientation and Carrying Modementioning

confidence: 99%

User Behaviour in the Wild: Analysing Naturalistic Log Data of a Pedestrian Navigation App

Jackermeier

Ludwig

2019

Adv. Cartogr. GIScience Int. Cartogr. Assoc.

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Abstract. Knowledge about real-life user behaviour is an important factor for the design of navigation systems. Prompted by the observation that users tend to use our campus navigation app in unexpected ways, we present a naturalistic study of navigation logs. The data set consists of sensor and interaction data from over 4600 sessions, collected over a span of several months from hundreds of users. In our analysis, we demonstrate how the core concepts from navigation literature, i. e. wayfinding and locomotion, can be observed, but also point out differences to previous studies and assumptions. One of our main findings is that the application is mostly used to plan routes in advance, and not to navigate along them. Furthermore, detailed case-studies of actual navigation sessions provide a unique insight into user behaviour and show that persons are often not focused on their navigation task but engaged otherwise. Based on these results, we formulate design implications that do not only apply to future iterations of our application, but can be seen as best practices for pedestrian navigation apps in general.

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Exploring the limits of PDR-based indoor localisation systems under realistic conditions

Cited by 8 publications

References 40 publications

Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review

Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review

Location-aware systems or location-based services: a survey with applications to CoViD-19 contact tracking

User Behaviour in the Wild: Analysing Naturalistic Log Data of a Pedestrian Navigation App

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