How feasible is the use of magnetic field alone for indoor positioning?

Li, Binghao; Gallagher, Thomas J.; Dempster, Andrew G.; Rizos, Chris

doi:10.1109/ipin.2012.6418880

Cited by 230 publications

(183 citation statements)

References 15 publications

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“…The data it used were easy to acquire and were less affected by the external environment. In the development of indoor positioning, geomagnetism has gradually attracted people's attention, and has become a research hotspot [81]. Geomagnetic positioning in indoor environments and buildings is mainly influenced by the steel skeleton structure.…”

Section: Geomagnetic-based Indoor Positioning Technologymentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

205

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: Geomagnetic-based Indoor Positioning Technologymentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

205

125

View full text Add to dashboard Cite

show abstract

“…This method of positioning is based on works on magnetic fields, the earth's magnetic field and the compass (B. Li, Gallagher, Dempster, & Rizos, 2012). Magnetic positioning system involves the use of magnetic signals for position determination within a magnetic field (Paperno, Sasada, & Leonovich, 2001;Blankenbach, Norrdine, & Hellmers, 2012;S.-E. Kim, Kim, Yoon, & Kim, 2012).…”

Section: Magnetic Positioning Systemmentioning

confidence: 99%

“…However, one issue with the magnetic positioning system is that of limited coverage (Mautz, 2012;B. Li et al, 2012).…”

Section: Challenges and Drawbacks Of Magnetic Positioningmentioning

confidence: 99%

“…This limitation affects the efficiency and robustness of the system, despite its high accuracy. In order to improve the coverage range, an increase in magnetic sensors and infrastructures may be needed to cover sufficient areas, hence increasing the complexity and cost of the system (Gu et al, 2009;B. Li et al, 2012).…”

Section: Challenges and Drawbacks Of Magnetic Positioningmentioning

confidence: 99%

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A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

149

129

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The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

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“…A comprehensive study is presented on design and hardware selection of robots [14,15]. Different researchers have come up with different localization technologies for indoor environment; WiFi [16], ultrasonic [17,18], RFID tags [19][20][21], FM [22], magnetic field [23,24], IMU [25], cameras, lasers and sonar [26,27]. Most of these technologies and solutions have some disadvantages and limitations especially when they are used in the indoor environment.…”

Section: Introductionmentioning

confidence: 99%

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

Ullah

Zhang

et al. 2018

Symmetry

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Abstract:A novel approach is proposed for the path tracking of a Wheeled Mobile Robot (WMR) in the presence of an unknown lateral slip. Much of the existing work has assumed pure rolling conditions between the wheel and ground. Under the pure rolling conditions, the wheels of a WMR are supposed to roll without slipping. Complex wheel-ground interactions, acceleration and steering system noise are the factors which cause WMR wheel slip. A basic research problem in this context is localization and slip estimation of WMR from a stream of noisy sensors data when the robot is moving on a slippery surface, or moving at a high speed. DecaWave based ranging system and Particle Filter (PF) are good candidates to estimate the location of WMR indoors and outdoors. Unfortunately, wheel-slip of WMR limits the ultimate performance that can be achieved by real-world implementation of the PF, because location estimation systems typically partially rely on the robot heading. A small error in the WMR heading leads to a large error in location estimation of the PF because of its cumulative nature. In order to enhance the tracking and localization performance of the PF in the environments where the main reason for an error in the PF location estimation is angular noise, two methods were used for heading estimation of the WMR (1): Reinforcement Learning (RL) and (2): Location-based Heading Estimation (LHE). Trilateration is applied to DecaWave based ranging system for calculating the probable location of WMR, this noisy location along with PF current mean is used to estimate the WMR heading by using the above two methods. Beside the WMR location calculation, DecaWave based ranging system is also used to update the PF weights. The localization and tracking performance of the PF is significantly improved through incorporating heading error in localization by applying RL and LHE. Desired trajectory information is then used to develop an algorithm for extracting the lateral slip along X-and Y-axis from the PF estimated position of the WMR, the lateral slip along X-and Y-axis is then used to take some corrective measures. Lateral slip information is also used to find the direction along which WMR has to move to get back along the desired trajectory. Simulation results show that our proposed LHE and RL heading estimation methods significantly improve the PF localization and tracking performance on a slippery surface in both indoor and outdoor environments. The simulation results also show that the accurate locations of WMR and desired path information are used to estimate and compensate the lateral slip.

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How feasible is the use of magnetic field alone for indoor positioning?

Cited by 230 publications

References 15 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

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