Indoor mapping for smart cities — An affordable approach: Using Kinect Sensor and ZED stereo camera

Gupta, Tanishq; Li, King Ho Holden

doi:10.1109/ipin.2017.8115909

Cited by 29 publications

(15 citation statements)

References 2 publications

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“…Spatial mapping creates a detailed representation of real-world surfaces of the environment so as holograms can interact with the physical environment. Spatial mapping uses sophisticated image processing and AI algorithms, and it may require the use of depth cameras [8].…”

Section: System Model Of Augmented-raysmentioning

confidence: 99%

“…This is usually performed by depth cameras that scan the environment of interest. Characteristic examples of such cameras are the Red Green Blue (RGB) depth (RGB-D) [8,10] and monocular cameras used for Simultaneously Localization and Mapping (SLAM) [11,12]. The existing spatial mapping algorithms create a mesh representation of the space that cannot be directly applied in ray tracing channel estimation techniques.…”

Section: Spatial Mapping and Facet Model Translation Enginementioning

confidence: 99%

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In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing

Koutitas

Siddaraju

Metsis

2020

Sensors

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This article presents a novel methodology for predicting wireless signal propagation using ray-tracing algorithms, and visualizing signal variations in situ by leveraging Augmented Reality (AR) tools. The proposed system performs a special type of spatial mapping, capable of converting a scanned indoor environment to a vector facet model. A ray-tracing algorithm uses the facet model for wireless signal predictions. Finally, an AR application overlays the signal strength predictions on the physical space in the form of holograms. Although some indoor reconstruction models have already been developed, this paper proposes an image to a facet algorithm for indoor reconstruction and compares its performance with existing AR algorithms, such as spatial understanding that are modified to create the required facet models. In addition, the paper orchestrates AR and ray-tracing techniques to provide an in situ network visualization interface. It is shown that the accuracy of the derived facet models is acceptable, and the overall signal predictions are not significantly affected by any potential inaccuracies of the indoor reconstruction. With the expected increase of densely deployed indoor 5G networks, it is believed that these types of AR applications for network visualization will play a key role in the successful planning of 5G networks.

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Section: System Model Of Augmented-raysmentioning

confidence: 99%

Section: Spatial Mapping and Facet Model Translation Enginementioning

confidence: 99%

In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing

Koutitas

Siddaraju

Metsis

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…For the motion tracking, we used the Stereolabs ZED (https://www.stereolabs.com/) stereo depth-sensing camera and inertial sensor that allows us to map our environment. By implementing SLAM (Simultaneous Localization and Mapping) [16,17] algorithm for environment mapping and object and determine the actual position of the user, which is widely used in navigation and robotics besides VR and AR applications.…”

Section: Vr Training Platformmentioning

confidence: 99%

Innovative technologies in training and education for maintenance team of NPPs

Soós

Balogh

Dobos

et al. 2019

EPJ Nuclear Sci. Technol.

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Many industries, such as nuclear power plants, chemical industry, oil and gas industry have dangerous working environments and hazardous conditions for employees. Maintenance, inspection and decommissioning activities in these safety-critical areas mean a serious risk, downtime is a significant financial loss. The Virtual Reality Training Platform is reflecting on this shortcoming, by providing the possibility for maintenance workers to be trained and prepared for unexpected scenarios, and to learn complex maintenance protocols without being exposed to unnecessary danger, like high temperature, radiation, etc. Employees can have training for equipment maintenance, dismantling of facilities at closed NPP Units. One of the most significant and unique added value of the immersive virtual reality solution is that the operator can experience lifelike emergencies (detonation, shutdown) under psychological pressure, while all of the physiology indicators can be monitored like eye-tracking. Users can work together anywhere in the world. A huge financial outage in industrial production is the preparation and maintenance downtime, which can be significantly reduced by the Virtual Training platform. This method can increase the accuracy, safety, reliability, and accountability of the maintenance and decommissioning procedures, while operational costs can be reduced as well.

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“…A 3D camera can be used for showing 360 views to enhance building visualisation and create 3D models of the scanned area. Gupta and Li [47] compared a 3D camera with Kinect, Faro, Leica and Tango companies' devices for indoor point cloud panoramas. Results showed that it is the only one that can scan 360 degrees in all directions quickly.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

et al. 2019

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The automated process of construction defect detection using non-contact methods provides vital information for quality control and updating building information modelling. The external cladding in modular construction should be regularly controlled in terms of the quality of panels and proper installation because its appearance is very important for clients. However, there are limited computational methods for examining the installation issues of external cladding remotely in an automated manner. These issues could be the incorrect sitting of a panel, unequal joints in an elevation, scratches or cracks on the face of a panel or dimensions of different elements of external cladding. This paper aims to present seven algorithms to detect panel edges and statistically compare their performance through application on two scenarios of buildings in construction sites. Two different scenarios are selected, where the building façades are available to the public, and a sample of 100 images is taken using a state-of-the-art 3D camera for edge detection analysis. The experimentation results are validated by using a series of computational error and accuracy analyses and statistical methods including Mean Square Error, Peak Signal to Noise Ratio and Structural Similarity Index. The performance of an image processing algorithm depends on the quality of images and the algorithm utilised. The results show better performance of the fuzzy logic algorithm because it detects clear edges for installed panels. The applications of classical operators including Sobel, Canny, LoG, Prewitt and Roberts algorithms give similar results and show similarities in terms of the average of errors and accuracy. In addition, the results show that the minor difference of the average of the error and accuracy indices for Sobel, Canny, LoG, Prewitt and Roberts methods between both scenarios are not statistically significant, while the difference in the average of the error and accuracy indices for RGB-Sobel and Fuzzy methods between both scenarios are statistically significant. The accuracy of the algorithms can be improved by removing unwanted items such as vegetation and clouds in the sky. The evaluated algorithms assist practitioners to analyse their images collected day to day from construction sites, and to update building information modelling and the project digital drawings. Future work may need to focus on the combination of the evaluated algorithms using new data sets including colour edge detection for automatic defect identification using RGB and 360-degree images.

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Indoor mapping for smart cities — An affordable approach: Using Kinect Sensor and ZED stereo camera

Cited by 29 publications

References 2 publications

In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing

In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing

Innovative technologies in training and education for maintenance team of NPPs

Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings

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