An Improved Indoor Positioning System Using RGB-D Cameras and Wireless Networks for Use in Complex Environments

Domingo, Jaime Duque; Cerrada, Carlos; Valero, Enrique; Cerrada, J.A.

doi:10.3390/s17102391

Cited by 26 publications

(18 citation statements)

References 30 publications

(40 reference statements)

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“…Como se ha comentado anteriormente, en este artículo se presenta una versión mejorada de un algoritmo de alta precisión en interiores [7], que a su vez generalizaba el algoritmo presentado en [6]. En dicho trabajo, se busca identificar y estimar la posición de personas en escenarios complejos habitados, donde se mueven simultáneamente.…”

Section: Funcionamiento Del Algoritmo Previounclassified

“…Una de las limitaciones del algoritmo presentado en [7] es que el número de usuarios detectados a través de las cámaras RGB-D debe ser igual al detectado utilizando WPS. En esta sección, se presenta la modificación del algoritmo para ser válido para cualquier situación.…”

Section: Algoritmo De Emparejamiento Extendidounclassified

“…El caso n 1 = n 2 = n corresponde al algoritmo presentado en [7], donde el número de usuarios detectados por cada uno de los dos sistemas es igual. Gráficamente, se puede ver en la Figura 2 una matriz S, donde n 1 > n 2 .…”

Section: Algoritmo De Emparejamiento Extendidounclassified

“…En este trabajo se presenta una mejora al algoritmo de posicionamiento en interiores (IPS, del inglés Indoor Positioning System) mediante sensores RGB-D y redes WiFi presentado por los autores en [7]. La mejora presentada hace referencia tanto al método de colocación de los sensores en el sistema universal de coordenadas (UCS, del inglés Universal Coordinate System), como a una limitación del algoritmo en cuanto al número de personas en seguimiento.…”

Section: Introductionunclassified

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Mejoras en el algoritmo de posicionamiento en interiores mediante sensores RGB-D y redes WiFi

Domingo¹,

Cerrada²,

Valero³

2020

Actas De Las XXXIX Jornadas De Automática, Badajoz, 5-7 De Septiembre De 2018

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Section: Funcionamiento Del Algoritmo Previounclassified

Section: Algoritmo De Emparejamiento Extendidounclassified

Section: Introductionunclassified

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Mejoras en el algoritmo de posicionamiento en interiores mediante sensores RGB-D y redes WiFi

Domingo¹,

Cerrada²,

Valero³

2020

Actas De Las XXXIX Jornadas De Automática, Badajoz, 5-7 De Septiembre De 2018

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“…Here the authors used a fiducial markers, in order to estimate the location within the environment. In [33], the authors used 20 Kinect cameras in order to perform tracking of multiple targets transiting various trajectories. This was done in conjuction with Wi-Fi collected through user-carried smartphones.…”

Section: Camera-based Sensorsmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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APS: A Large-Scale Multi-modal Indoor Camera Positioning System

Ghofrani

Toroghi

Tabatabaie³

2021

Pattern Recognition and Artificial Intelligence

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Navigation inside a closed area with no GPS-signal accessibility is a highly challenging task. In order to tackle this problem, recently the imaging-based methods have grabbed the attention of many researchers. These methods either extract the features (e.g. using SIFT, or SOSNet) and map the descriptive ones to the camera position and rotation information, or deploy an end-to-end system that directly estimates this information out of RGB images, similar to PoseNet. While the former methods suffer from heavy computational burden during the test process, the latter suffers from lack of accuracy and robustness against environmental changes and object movements. However, end-to-end systems are quite fast during the test and inference and are pretty qualified for real-world applications, even though their training phase could be longer than the former ones. In this paper, a novel multi-modal end-to-end system for large-scale indoor positioning has been proposed, namely APS (Alpha Positioning System), which integrates a Pix2Pix GAN network to reconstruct the point cloud pair of the input query image, with a deep CNN network in order to robustly estimate the position and rotation information of the camera. For this integration, the existing datasets have the shortcoming of paired RGB/point cloud images for indoor environments. Therefore, we created a new dataset to handle this situation. By implementing the proposed APS system, we could achieve a highly accurate camera positioning with a precision level of less than a centimeter.

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An Improved Indoor Positioning System Using RGB-D Cameras and Wireless Networks for Use in Complex Environments

Cited by 26 publications

References 30 publications

Mejoras en el algoritmo de posicionamiento en interiores mediante sensores RGB-D y redes WiFi

Mejoras en el algoritmo de posicionamiento en interiores mediante sensores RGB-D y redes WiFi

Sensor Modalities and Fusion for Robust Indoor Localisation

APS: A Large-Scale Multi-modal Indoor Camera Positioning System

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