On Monte Carlo smoothing in multi sensor indoor localisation

Fetzer, Toni; Ebner, Frank; Deinzer, Frank; Köping, Lukas; Grzegorzek, Marcin

doi:10.1109/ipin.2016.7743670

Cited by 23 publications

(16 citation statements)

References 20 publications

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“…Fetzer et al [37] proposed a strategy that combined varieties of assistive technology aimed at multi-floor indoor environments. The fingerprint data they used is described in Equation (1).…”

Section: Empirical Modelingmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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Section: Empirical Modelingmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

View full text Add to dashboard Cite

show abstract

“…Since then, this technique has been extended by including prior navigation knowledge using realistic human walking paths [51] and adding smoothing methods [40]. Additionally, a self-developed map editor allows for creating advanced 3D maps and realistically shaped stairs.…”

Section: Description Of the Competing Ipsmentioning

confidence: 99%

“…Finally, a fixed interval smoother, using backward simulation, is deployed for further optimisation and to reduce multimodalities [40]. Here, a smoothing transition model compares the distance, angle and height between some future and the current state.…”

Section: Description Of the Competing Ipsmentioning

confidence: 99%

The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work

Torres-Sospedra

Jiménez

Knauth

et al. 2017

Sensors

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This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors’ estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.

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“…The smartphone's accelerometer, gyroscope, magnetometer, GPS-and Wi-Fi-module provide the observations for both the transition and the following evaluation step to infer the hidden state, namely the pedestrian's location and heading [20,21].…”

Section: Indoor Positioning Systemmentioning

confidence: 99%

On Wi-Fi Model Optimizations for Smartphone-Based Indoor Localization

Ebner

Fetzer

Deinzer

et al. 2017

IJGI

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Indoor localization and indoor pedestrian navigation is an active field of research with increasing attention. As of today, many systems will run on commercial smartphones, but most of them still rely on fingerprinting, which demands high setup and maintenance times. Alternatives, such as simple signal strength prediction models, provide fast setup times, but often do not provide the accuracy required for use cases like indoor navigation or location-based services. While more complex models provide an increased accuracy by including architectural knowledge about walls and other obstacles, they often require additional computation during runtime and demand prior knowledge during setup. Within this work, we will thus focus on simple, easy to set up models and evaluate their performance compared to real-world measurements. The evaluation ranges from a fully-empiric, instant setup, given that the transmitter locations are well known, to a highly optimized scenario based on some reference measurements within the building. Furthermore, we will propose a new signal strength prediction model as a combination of several simple ones. This tradeoff increases accuracy with only minor additional computations. All of the optimized models are evaluated within an actual smartphone-based indoor localization system. This system uses the phone's Wi-Fi, barometer and IMU to infer the pedestrian's current location via recursive density estimation based on particle filtering. We will show that while a 100 % empiric parameter choice for the model already provides enough accuracy for many use cases, a small number of reference measurements is enough to dramatically increase such a system's performance.

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On Monte Carlo smoothing in multi sensor indoor localisation

Cited by 23 publications

References 20 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work

On Wi-Fi Model Optimizations for Smartphone-Based Indoor Localization

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