Seamless Navigation, 3D Reconstruction, Thermographic and Semantic Mapping for Building Inspection

Schischmanow, Adrian; Dahlke, Dennis; Baumbach, Dirk; Ernst, Ines; Linkiewicz, Magdalena

doi:10.3390/s22134745

Cited by 9 publications

(7 citation statements)

References 47 publications

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“…To obtain an indoor scan of the building, a camera system or laser scanner can also be applied. For this purpose, we propose using the Integrated Positioning System (IPS), 13 a multisensor approach that includes a low-cost inertial measurement unit combined with a stereo camera system. The combined map of the MACS and the IPS can then be used for indoor navigation.…”

Section: Overview Of the Systemmentioning

confidence: 99%

An Overall First Responder Tracking and Coordination Framework

Kaiser,

Sand,

Linkiewicz

et al. 2023

IT Prof.

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Section: Overview Of the Systemmentioning

confidence: 99%

An Overall First Responder Tracking and Coordination Framework

Kaiser,

Sand,

Linkiewicz

et al. 2023

IT Prof.

Self Cite

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“…The obtained results allowed for solving technological barriers related to software interoperability. Schischmanow et al [34] realized seamless navigation, 3D reconstruction, and thermographic and semantic mapping for building inspection for the case study of a house with surroundings in the village of Morschenich near the city of Jülich, North Rhine-Westphalia, Germany.…”

Section: Introductionmentioning

confidence: 99%

3D Modeling of a Virtual Built Environment Using Digital Tools: Kilburun Fortress Case Study

2023

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The reliable reconstruction of cultural or historical heritage objects is an urgent problem for humanity. It can be successfully solved using up-to-date 3D modeling tools. The proposed technique allows for modeling virtual environments at an even higher level. This study aimed to develop an approach for designing historical heritage objects with sufficient accuracy using a built environment. The Kilburun Fortress was chosen as the object of study. The modeling procedure includes monitoring the object’s territory, analyzing archival, librarian, and cartographic sources, and further modeling and reproducing the research object in a virtual environment using various software tools. The following stages were implemented during this study: analysis and processing of preliminary data (analysis of plans and schemes, overlapping maps); the scaling of graphical objects for the reliable reproduction of the studied object; the design of a working 3D model using AutoCAD and SketchUp; the rendering and final processing of textures using Quixel; and visualization using Twinmotion. As a result, a model of the historical heritage object was created using 3D means. The model can also be integrated into ArchiCAD and Revit software.

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“…The thermal sensor proved to be versatile under different weather conditions. One study used the fusion of images from a stereo camera and the thermal images from a thermal sensor for building inspection [ 25 ]. A novel approach to geometrically calibrate and pixel-co-register the trifocal camera system was presented here by the authors.…”

Section: Introductionmentioning

confidence: 99%

Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data

Raju

Imtiaz

Sazonov

2023

Sensors

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Sensor-based food intake monitoring has become one of the fastest-growing fields in dietary assessment. Researchers are exploring imaging-sensor-based food detection, food recognition, and food portion size estimation. A major problem that is still being tackled in this field is the segmentation of regions of food when multiple food items are present, mainly when similar-looking foods (similar in color and/or texture) are present. Food image segmentation is a relatively under-explored area compared with other fields. This paper proposes a novel approach to food imaging consisting of two imaging sensors: color (Red–Green–Blue) and thermal. Furthermore, we propose a multi-modal four-Dimensional (RGB-T) image segmentation using a k-means clustering algorithm to segment regions of similar-looking food items in multiple combinations of hot, cold, and warm (at room temperature) foods. Six food combinations of two food items each were used to capture RGB and thermal image data. RGB and thermal data were superimposed to form a combined RGB-T image and three sets of data (RGB, thermal, and RGB-T) were tested. A bootstrapped optimization of within-cluster sum of squares (WSS) was employed to determine the optimal number of clusters for each case. The combined RGB-T data achieved better results compared with RGB and thermal data, used individually. The mean ± standard deviation (std. dev.) of the F1 score for RGB-T data was 0.87 ± 0.1 compared with 0.66 ± 0.13 and 0.64 ± 0.39, for RGB and Thermal data, respectively.

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Seamless Navigation, 3D Reconstruction, Thermographic and Semantic Mapping for Building Inspection

Cited by 9 publications

References 47 publications

An Overall First Responder Tracking and Coordination Framework

An Overall First Responder Tracking and Coordination Framework

3D Modeling of a Virtual Built Environment Using Digital Tools: Kilburun Fortress Case Study

Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data

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