360-Degree Tri-Modal Scanning: Engineering a Modular Multi-Sensor Platform for Semantic Enrichment of BIM Models
| Autor: | F. C. Collins, F. Noichl, M. Slepicka, G. Cones, A. Borrmann |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-2024, Pp 41-48 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2194-9042 2194-9050 |
| DOI: | 10.5194/isprs-annals-X-1-2024-41-2024 |
| Popis: | Point clouds, image data, and corresponding processing algorithms are intensively investigated to create and enrich Building Information Models (BIM) with as-is information and maintain their value across the building lifecycle. Point clouds can be captured using LiDAR and enriched with color information from images. Complementary to such dual-sensor systems, thermography captures the infrared light spectrum, giving insight into the temperature distribution on an object’s surface and allowing a diagnosis of the as-is energetic health of buildings beyond what humans can see. Although the three sensor modes are commonly used in pair-wise combinations, only a few systems leveraging the power of tri-modal sensor fusion have been proposed. This paper introduces a sensor system comprising LiDAR, RGB, and a radiometric thermal infrared sensor that can capture a 360-degree range through bi-axial rotation. The resulting tri-modal data is fused to a thermo-color point cloud from which temperature values are derived for a standard indoor building setting. Qualitative data analysis shows the potential for unlocking further object semantics in a state-of-the-art Scan-to-BIM pipeline. Furthermore, an outlook is provided on the cross-modal usage of semantic segmentation for automatic, accurate temperature calculations. |
| Databáze: | Directory of Open Access Journals |
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