scholarly journals Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching

2019 ◽  
Vol 9 (20) ◽  
pp. 4260 ◽  
Author(s):  
Patrick Herbers ◽  
Markus König
Keyword(s):  
Augmented Reality ◽  
Mobile Devices ◽  
Template Matching ◽  
High Speed ◽  
Indoor Localization ◽  
Contextual Information ◽  
Point Clouds ◽  
Distance Map ◽  
Information Display ◽  
Localization Accuracy

Mobile devices are a common target for augmented reality applications, especially for showing contextual information in buildings or construction sites. A prerequisite of contextual information display is the localization of objects and the device in the real world. In this paper, we present our approach to the problem of mobile indoor localization with a given building model. The approach does not use external sensors or input. Accurate external sensors such as stationary cameras may be expensive and difficult to set up and maintain. Relying on already existing external sources may also prove to be difficult, as especially inside buildings, Internet connections can be unreliable and GPS signals can be inaccurate. Therefore, we try to find a localization solution for augmented reality devices that can accurately localize itself only with data from internal sensors and preexisting information about the building. If a building has an accurate model of its geometry, we can use modern spatial mapping techniques and point-cloud matching to find a mapping between local device coordinates and global model coordinates. We use normal analysis and 2D template matching on an inverse distance map to determine this mapping. The proposed algorithm is designed to have a high speed and efficiency, as mobile devices are constrained by hardware limitations. We show an implementation of the algorithm on the Microsoft HoloLens, test the localization accuracy, and offer use cases for the technology.

scholarly journals Augmented Reality and Mobile Devices: A Binominal Methodological Resource for Inclusive Education (SDG 4). An Example in Secondary Education

2018 ◽  
Vol 10 (10) ◽  
pp. 3446 ◽  
Author(s):  
Francisco del Cerro Velázquez ◽  
Ginés Morales Méndez
Keyword(s):  
Sustainable Development ◽  
Secondary Education ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Inclusive Education ◽  
Contextual Information ◽  
Quality Education ◽  
Conceptual Approach ◽  
Development Goals ◽  
The Way

The rise of so-called emerging technologies is broadening the way in which students access information and in turn changing the way in which they can interact and the experiences to which they are exposed. Mobile devices are regarded as flexible tools that facilitate access to information in different formats and in any environment. For its part, Augmented Reality is a technique that, through mobile tools, can enhance the globalization of content and access to contextual information in various ways. Together, the globalization of mobile devices and Augmented Reality contribute to an inclusive, equitable, and quality education, as mentioned by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) in goal four on Sustainable Development (Sustainable Development Goals 4 (SDG 4)). This article analyses the binomial Augmented Reality-mobile devices, and takes a conceptual approach to these technological environments, both the technique and the tool, in the context of quality education. To assess the potential of Augmented Reality-mobile devices as a methodological learning resource, a learning unit of Secondary Education is presented in the field of Technology, enriched with different materials related with Augmented Reality.

scholarly journals Robust autonomous flight in cluttered environment using a depth sensor

2020 ◽  
Vol 12 ◽  
pp. 175682932092452
Author(s):  
Liang Lu ◽  
Alexander Yunda ◽  
Adrian Carrio ◽  
Pascual Campoy
Keyword(s):  
High Speed ◽  
Point Clouds ◽  
Depth Sensor ◽  
Cluttered Environments ◽  
Industrial Plants ◽  
Cluttered Environment ◽  
Navigation Strategy ◽  
Physical Sensors ◽  
Aerial Vehicle ◽  
Planning Objective

This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

scholarly journals Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
VanDung Nguyen ◽  
Tran Trong Khanh ◽  
Tri D. T. Nguyen ◽  
Choong Seon Hong ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Computing System ◽  
Mobile User ◽  
Computation Offloading ◽  
Context Aware ◽  
Computing Systems ◽  
Iot Applications ◽  
Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

DAFI

2021 ◽  
Vol 5 (4) ◽  
pp. 1-21
Author(s):  
Hang Li ◽  
Xi Chen ◽  
Ju Wang ◽  
Di Wu ◽  
Xue Liu
Keyword(s):  
Indoor Localization ◽  
Domain Adaptation ◽  
Target Domain ◽  
Localization Accuracy ◽  
Source Domain ◽  
Fine Grained ◽  
Time Signals ◽  
Environmental Variations ◽  
Device Free ◽  
Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

Circuit Breaker Mechanical Characteristic Parameters Measurement Based on Machine Vision

2014 ◽  
Vol 687-691 ◽  
pp. 934-937
Author(s):  
Shu Tao Zhao ◽  
Yu Tao Xu ◽  
Zhi Wan Cheng ◽  
Jian Feng Ren ◽  
Dan Jiang
Keyword(s):  
Mechanical Characteristic ◽  
Template Matching ◽  
High Speed ◽  
Circuit Breaker ◽  
Motion Pictures ◽  
Time Interval ◽  
Connecting Rod ◽  
Characteristic Parameters ◽  
Coordinate Changes ◽  
Parameters Measurement

Aim at the disadvantages of traditional circuit breaker mechanical characteristic parameters test. Get the motion pictures of insulation connecting rod through high-speed camera, using the finite difference method to quickly screen out the motion pictures, and selecting punctuation area as a template for learning, using non-uniform sampling have a template matching, obtain the center coordinates of matching results, time interval is known every frame. Through coordinate changes over time we can obtain mechanical parameters of the circuit breaker accurately, fast, conveniently. Lab VIEW programs achieve the above process automatically.

Augmented Reality and Mobile Devices as Tools to Enhance Spatial Vision in Graphic Representations

2020 ◽  
pp. 420-427
Author(s):  
T. Polhmann ◽  
D. Parras-Burgos ◽  
F. Cavas-Martínez ◽  
F. J. F. Cañavate ◽  
J. Nieto ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Mobile Devices ◽  
Spatial Vision ◽  
Graphic Representations

scholarly journals DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chong Han ◽  
Wenjing Xun ◽  
Lijuan Sun ◽  
Zhaoxiao Lin ◽  
Jian Guo
Keyword(s):  
Indoor Localization ◽  
Wireless Sensing ◽  
Training Phase ◽  
Training Time ◽  
Localization Accuracy ◽  
Localization System ◽  
Complex Models ◽  
Indoor Scenarios ◽  
Trained Network ◽  
Localization Systems

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

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