scholarly journals Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation

2021 ◽  
Vol 11 (19) ◽  
pp. 9187
Author(s):  
Tae-Won Jung ◽  
Chi-Seo Jeong ◽  
Soon-Chul Kwon ◽  
Kye-Dong Jung
Keyword(s):  
Neural Network ◽  
Augmented Reality ◽  
Mixed Reality ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Visual Localization ◽  
Positioning System ◽  
Point Graph ◽  
Indoor Space ◽  
Visual Positioning

Indoor localization is a basic element in location-based services (LBSs), including seamless indoor and outdoor navigation, location-based precision marketing, spatial recognition in robotics, augmented reality, and mixed reality. The popularity of LBSs in the augmented reality and mixed reality fields has increased the demand for a stable and efficient indoor positioning method. However, the problem of indoor visual localization has not been appropriately addressed, owing to the strict trade-off between accuracy and cost. Therefore, we use point cloud and RGB characteristic information for the accurate acquisition of three-dimensional indoor space. The proposed method is a novel visual positioning system (VPS) capable of determining the user’s position by matching the pose information of the object estimated by the improved point-graph neural network (GNN) with the pose information label of a voxel database object addressed in predefined voxel units. We evaluated the performance of the proposed system considering a stationary object in indoor space. The results verify that high positioning accuracy and direction estimation can be efficiently achieved. Thus, spatial information of indoor space estimated using the proposed novel VPS can aid in indoor navigation.

scholarly journals Development of an Omnidirectional-Image-Based Data Model through Extending the IndoorGML Concept to an Indoor Patrol Service

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Hyo-jin Jung ◽  
Jiyeong Lee
Keyword(s):  
Data Model ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Location ◽  
Indoor Space ◽  
Omnidirectional Image ◽  
Space Data ◽  
Omnidirectional Images ◽  
Indoor Spaces ◽  
Topological Data

Different indoor representation methods have been studied for their ability to provide indoor location-based services (LBS). Among them, omnidirectional imaging is one of the most typical and simple methods for representing an indoor space. However, a georeferenced omnidirectional image cannot be used for simple attribute searches, spatial queries, and spatial awareness analyses. To perform these functions, topological data are needed to define the features of and spatial relationships among spatial objects including indoor spaces as well as facilities like CCTV cameras considered in patrol service applications. Therefore, this study proposes an indoor space application data model for an indoor patrol service that can implement functions suited to linking indoor space data and service objects. In order to do this, the study presents a method for linking data between omnidirectional images representing indoor spaces and topological data on indoor spaces based on the concept of IndoorGML. Also, we conduct an experimental implementation of the integrated 3D indoor navigation model for patrol service using GIS data. Based on the results, we evaluate the benefits of using such a 3D data fusion method that integrates omnidirectional images with vector-based topological data models based on IndoorGML for providing indoor LBS in built environments.

scholarly journals INDOORGML – A STANDARD FOR INDOOR SPATIAL MODELING

2016 ◽  
Vol XLI-B4 ◽  
pp. 701-704
Author(s):  
Ki-Joune Li
Keyword(s):  
Spatial Data ◽  
Geometric Model ◽  
Spatial Models ◽  
Cell Number ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Space ◽  
Outdoor Spaces ◽  
Core Module ◽  
Outdoor Space

With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

scholarly journals INDOORGML – A STANDARD FOR INDOOR SPATIAL MODELING

2016 ◽  
Vol XLI-B4 ◽  
pp. 701-704 ◽  
Author(s):  
Ki-Joune Li
Keyword(s):  
Spatial Data ◽  
Geometric Model ◽  
Spatial Models ◽  
Cell Number ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Space ◽  
Outdoor Spaces ◽  
Core Module ◽  
Outdoor Space

With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

Improved trilateration for indoor localization: Neural network and centroid-based approach

2021 ◽  
Vol 17 (11) ◽  
pp. 155014772110539
Author(s):  
Satish R Jondhale ◽  
Amruta S Jondhale ◽  
Pallavi S Deshpande ◽  
Jaime Lloret
Keyword(s):  
Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Iteration Step ◽  
Generalized Regression Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Generalized Regression

Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network–based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm.

scholarly journals SLAM-based Indoor Navigation in University Buildings

2021 ◽  
Author(s):  
Ekaterina Sukhareva ◽  
Tatiana Tomchinskaya ◽  
Ilya Serov
Keyword(s):  
Augmented Reality ◽  
Indoor Navigation ◽  
Positioning System ◽  
Navigation Systems ◽  
Mobile Platforms ◽  
Localization And Mapping ◽  
Mapping Technology ◽  
Augmented Reality Technology ◽  
University Buildings ◽  
Indoor Positioning System

The article discusses the use of SLAM (simultaneous localization and mapping) technology, with the help of which it is possible to build Indoor navigation systems using augmented reality technology, including on mobile platforms. The article also provides an overview of the positive and negative aspects of the SLAM technology its principle of operation for positioning a navigator using augmented reality in a university building within the framework of a student project are reviewed. The already implemented projects on similar topics, but using other technologies are considered their features are described. An example of the implementation of an indoor positioning system in a university using SLAM is given.

scholarly journals Integrating Image and Network-Based Topological Data through Spatial Data Fusion for Indoor Location-Based Services

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Dasol Ahn ◽  
Alexis Richard C. Claridades ◽  
Jiyeong Lee
Keyword(s):  
Data Fusion ◽  
Spatial Data ◽  
Urban Areas ◽  
Spatial Information ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Data Conversion ◽  
Position Information ◽  
Indoor Space ◽  
Topological Data

Nowadays, the importance and utilization of spatial information are recognized. Particularly in urban areas, the demand for indoor spatial information draws attention and most commonly requires high-precision 3D data. However accurate, most methodologies present problems in construction cost and ease of updating. Images are accessible and are useful to express indoor space, but pixel data cannot be applied directly to provide indoor services. A network-based topological data gives information about the spatial relationships of the spaces depicted by the image, as well as enables recognition of these spaces and the objects contained within. In this paper, we present a data fusion methodology between image data and a network-based topological data, without the need for data conversion, use of a reference data, or a separate data model. Using the concept of a Spatial Extended Point (SEP), we implement this methodology to establish a correspondence between omnidirectional images and IndoorGML data to provide an indoor spatial service. The proposed algorithm used position information identified by a user in the image to define a 3D region to be used to distinguish correspondence with the IndoorGML and indoor POI data. We experiment with a corridor-type indoor space and construct an indoor navigation platform.

scholarly journals Human-Centered Design Science Research Evaluation for Gamified Augmented Reality

2021 ◽  
Vol 2 ◽  
Author(s):  
Nikolche Vasilevski ◽  
James Birt
Keyword(s):  
Augmented Reality ◽  
Mixed Reality ◽  
Research Evaluation ◽  
Design Science ◽  
Evaluation Process ◽  
Science Research ◽  
Design Science Research ◽  
Location Based Services ◽  
Human Centered Design ◽  
Evaluation Activity

As augmented reality (AR) and gamification design artifacts for education proliferate in the mobile and wearable device market, multiple frameworks have been developed to implement AR and gamification. However, there is currently no explicit guidance on designing and conducting a human-centered evaluation activity beyond suggesting possible methods that could be used for evaluation. This study focuses on human-centered design evaluation pattern for gamified AR using Design Science Research Methodology (DSRM) to support educators and developers in constructing immersive AR games. Specifically, we present an evaluation pattern for a location-based educational indigenous experience that can be used as a case study to support the design of augmented (or mixed) reality interfaces, gamification implementations, and location-based services. This is achieved through the evaluation of three design iterations obtained in the development cycle of the solution. The holistic analysis of all iterations showed that the evaluation process could be reused, evolved, and its complexity reduced. Furthermore, the pattern is compatible with formative and summative evaluation and the technical or human-oriented types of evaluation. This approach provides a method to inform the evaluation of gamified AR apps. At the same time, it will enable a more approachable evaluation process to support educators, designers, and developers.

scholarly journals Gated Recurrent Unit with RSSIs from Heterogeneous Network for Mobile Positioning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Junxiang Wang ◽  
Canyang Guo ◽  
Ling Wu
Keyword(s):  
Neural Network ◽  
Mobile Devices ◽  
Heterogeneous Network ◽  
Cellular Network ◽  
Location Based Services ◽  
Series Data ◽  
Positioning System ◽  
Mobile Positioning ◽  
Gated Recurrent Unit ◽  
Research Studies

Recently, research studies on Location-Based Services (LBSs) based on networks including cellular network and Wi-Fi network have gradually become popular. Received Signal Strength Indicators (RSSIs) from the network can be detected and collected by mobile devices to estimate the locations without adopting the Global Positioning System (GPS). Previous research studies utilized the RSSIs of only cellular network or only Wi-Fi network to estimate location, which leads to a two-fold predicament involving error limits of cellular network-based methods and environmental constraints of Wi-Fi network-based methods. In addition, accommodating a highly temporal dependence of RSSI series data, this paper proposed a mobile positioning system based on Gated Recurrent Unit (GRU) with RSSIs from the heterogeneous network. GRU learns the temporal correlation of RSSIs and the relationship between RSSIs and GPS coordinates to estimate the locations of mobile devices. A large number of real experiments have been carried out to verify the performance of the proposed method, and experimental results demonstrate that the proposed method has lower errors (i.e., 5.86 m and 75% of errors within 4 m) compared with Neural Network (NN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).

MapIN - Make a Path Inside

2021 ◽  
Vol 9 (VII) ◽  
pp. 3318-3323
Author(s):  
Dr. Sachi Gupta
Keyword(s):  
Global Positioning System ◽  
Indoor Navigation ◽  
Positioning System ◽  
Technological Aspect ◽  
Positioning Systems ◽  
Internet Technologies ◽  
Indoor Space ◽  
Wide Range ◽  
Global Positioning ◽  
Airport Terminal

Our aim is to provide a comprehensive summary of the evolution of indoor navigation technologies. Indoor navigation is a system that is used to locate the exact locations inside a campus. To provide a technological aspect of indoor positioning systems, there are a wide range of technologies and approaches. This system does not use the GPS (Global Positioning System) and any other Internet technologies. The aim is to create an app that would show the users a navigation route in the real world via mobile device’s screen. This can be beneficial to citizens in their day to day life as it allows the user to precisely navigate to a specific location in an architecture they have never been to before, such as an airport terminal, a classroom or the library in a campus, malls/retail store to navigate the customers to the items they would like to purchase etc. The created framework ought to give, progressively, valuable route data that empowers a user to settle on reasonable and convenient choices on which course to continue in an indoor space.

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