The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-M-2-2023
https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1209-2023
https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1209-2023
26 Jun 2023
 | 26 Jun 2023

A MULTI-SENSOR APPROACH TO SURVEY COMPLEX ARCHITECTURES SUPPORTED BY MULTI-CAMERA PHOTOGRAMMETRY

L. Perfetti, F. Spettu, C. Achille, F. Fassi, C. Navillod, and C. Cerutti

Keywords: Cultural heritage, Data fusion, Point cloud, Photogrammetry, Laser scanning, UAV, Multicamera

Abstract. Point clouds are nowadays a standard format of three-dimensional data. Various survey techniques are available, differing in characteristics, mode of use, and target applications, nevertheless producing point clouds that are similar, comparable, and combinable. According to recent literature, combining data from multiple sensors is an established practice for large surveying projects, particularly in Cultural Heritage, where the geometric complexity of buildings encourages the employment of many sensors. This paper presents a multi-sensor approach to surveying complex architectural spaces. The case study is the Cathedral of Aosta (AO) in Italy, which is interested in a conservation project that requires investigating the two bell towers of the cathedral. The survey aimed to produce a point cloud of 5 mm resolution and 1–2 cm accuracy compatible with the 1:50 scale of representation. The following survey techniques were employed: (i) laser scanning, (ii) terrestrial photogrammetry, (iii) UAV photogrammetry, and (iv) multi-camera fisheye photogrammetry. The distinctive feature of our approach lies in the multi-camera survey, conducted using a prototype composed of five fisheye cameras. The paper describes the data acquisition phase conducted with the different techniques, the mutual verification of the data performed by cross-sections check, the segmentation, and the final assembly of the various portions until a complete point cloud with homogeneous characteristics is obtained. All the data were then collected in a web platform (FlyVast) enriched with data and info made available to the professional to plan future interventions.