Investigating effective methods for integration of building's map with low cost inertial sensors and wifi-based positioning

Khan, Muhammad Irshan; Syrjärinne, Jari

doi:10.1109/ipin.2013.6817847

Cited by 18 publications

(11 citation statements)

References 11 publications

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“…For instance, data fusion occurs also with different types of RF sensors to improve the localization accuracy since different positioning systems may complement each other [91].…”

Section: Hybrid Data Fusionmentioning

confidence: 99%

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

Paul

Sato

2017

JSAN

182

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Abstract:Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today's communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

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“…For instance, data fusion occurs also with different types of RF sensors to improve the localization accuracy since different positioning systems may complement each other [91].…”

Section: Hybrid Data Fusionmentioning

confidence: 99%

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

Paul

Sato

2017

JSAN

182

View full text Add to dashboard Cite

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“…In this case, a relative localization can be obtained with respect to the last known absolute position and better accuracy can be achieved. This mainly included designing wearable systems that track the location of mobile users using (1) IMUs fused with RFID tags placed around the smart environment [38,63], or 2) IMU data fused with WLAN-based positioning data [27,41], or 3) IMU data fused with GPS-based localization data [3,10,19,47]. The integration of inertial navigation systems and infrastructure-based tracking systems has been extensively applied in a recent effort in the field of construction [2] whereby three approaches were proposed.…”

Section: Motion Sensing Technologiesmentioning

confidence: 99%

Infrastructureless approach for ubiquitous user location tracking in construction environments

Khoury

Chdid

Oueis

et al. 2015

Automation in Construction

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“…Similarly, Jin et al [31] proposed to reduce the uncertainty of the PDR result based on sparse and partial locations sampled from the available wireless signals. In addition to this, this map information and particle filter are often combined with PDR to enhance the accuracy by implying possible routes and barriers, thus eliminating invalid particles [8,32]. Bojja et al [33] extended the particle filter to three dimensions and combined it with collision detection techniques to navigate and localize vehicles in a parking garage.…”

Section: Introductionmentioning

confidence: 99%

“…Bao et al [14] computed the geometric similarity between a trace of the user trajectory and the map (the length of the straight line and the angle of turning) to reference the latest corner, and hence eliminated the error caused by gyroscope noise. Khan et al [32] defined pathways (e.g., hallways) and barriers (e.g., walls) that the user can traverse and cannot pass, respectively. Combined with the particle filter, PDR, and Wi-Fi scans, these methods can achieve zero-effort calibration for Wi-Fi fingerprinting.…”

Section: Introductionmentioning

confidence: 99%

Robust Indoor Mobile Localization with a Semantic Augmented Route Network Graph

Zhou¹,

Zheng²,

Xiong³

et al. 2017

IJGI

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Abstract:In recent years, using smartphones to determine pedestrian locations in indoor environments is an extensively promising technique for improving context-aware applications. However, the applicability and accuracy of the conventional approaches are still limited due to infrastructure-dependence, and there is seldom consideration of the semantic information inherently existing in maps. In this paper, a semantically-constrained low-complexity sensor fusion approach is proposed for the estimation of the user trajectory within the framework of the smartphone-based indoor pedestrian localization, which takes into account the semantic information of indoor space and its compatibility with user motions. The user trajectory is established by pedestrian dead reckoning (PDR) from the mobile inertial sensors, in which the proposed semantic augmented route network graph with adaptive edge length is utilized to provide semantic constraint for the trajectory calibration using a particle filter algorithm. The merit of the proposed method is that it not only exploits the knowledge of the indoor space topology, but also exhausts the rich semantic information and the user motion in a specific indoor space for PDR accumulation error elimination, and can extend the applicability for diverse pedestrian step length modes. Two experiments are conducted in the real indoor environment to verify of the proposed approach. The results confirmed that the proposed method can achieve highly acceptable pedestrian localization results using only the accelerometer and gyroscope embedded in the phones, while maintaining an enhanced accuracy of 1.23 m, with the indoor semantic information attached to each pedestrian's motion.

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Investigating effective methods for integration of building's map with low cost inertial sensors and wifi-based positioning

Cited by 18 publications

References 11 publications

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

Infrastructureless approach for ubiquitous user location tracking in construction environments

Robust Indoor Mobile Localization with a Semantic Augmented Route Network Graph

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