The paper outlines the first steps of a research project focused on the digitalization of underground tunnels for the mining industry. The aim is to solve the problem of rapidly, semi-automatically, efficiently, and reliably digitizing complex and meandering tunnels. A handheld multi-camera photogrammetric tool is used for the survey phase, which allows for the rapid acquisition of the image dataset needed to produce the 3D data. Moreover, since often, automatic, and fast acquisitions are not supported by easy-to-use tools to access and use the data at an operational level, a second aim of the research is to define a method able to arrange and organise the gathered data so that it would be easily accessible. The proposed approach is to compute the 3D skeleton of the surveyed environment by employing tools developed for the analysis of vascular networks in medical imagery. From the computed skeletonization of the underground tunnels, a method is proposed to automatically extrapolate valuable information such as cross-sections, decomposed portions of the tunnel, and the referenced images from the photogrammetric survey. The long-term research goal is to create an effective workflow, both at the hardware and software level, that can reduce computation times, process large amounts of data, and reduce dependency on high levels of experience.

The advent of mobile mapping systems (MMSs) and computer vision algorithms has enriched a wide range of navigation and mapping tasks such as localisation, 3D motion estimation and 3D mapping. This study focuses on Visual Simultaneous Localisation and Mapping (V-SLAM) in the context of two in-houses MMSs: Ant3D, a patented five-fisheye multi-camera rig and GeoRizon, a high-resolution stereo fisheye rig. The aim is to leverage V-SLAM to enhance the systems performance in near-real-time and non-real-time 3D reconstruction applications. The research investigates both Monocular and Stereo V-SLAM applied to both MMSs and tackles the challenge of combining the V-SLAM estimated trajectory of one or a pair of cameras with known multi-camera relative orientation. We propose a state-of-the-art code that serves as a flexible and extensible platform for MMSs image acquisition and processing, along with an adapted version of the well-established ORB-SLAM3.0. Evaluation is performed in a cultural heritage challenging setup: the Minguzzi spiral staircase in the Duomo di Milano Cathedral. Performed tests highlight that introducing V-SLAM trajectories as well as pre-calibrated interior orientation and multi-camera constraints improve speed, applicability and accuracy of 3D surveys.

Multi-camera Visual Simultaneous Localization and Mapping (V-SLAM) increases spatial coverage through multi-view image streams, improving localization accuracy and reducing data acquisition time. Despite its speed and generally robustness, V-SLAM often struggles to achieve precise camera poses necessary for accurate 3D reconstruction, especially in complex environments. This study introduces two novel multi-camera optimization methods to enhance pose accuracy, reduce drift, and ensure loop closures. These methods refine multi-camera V-SLAM outputs within existing frameworks and are evaluated in two configurations: (1) multiple independent stereo V-SLAM instances operating on separate camera pairs; and (2) multi-view odometry processing all camera streams simultaneously. The proposed optimizations include (1) a multi-view feature-based optimization that integrates V-SLAM poses with rigid inter-camera constraints and bundle adjustment; and (2) a multi-camera pose graph optimization that fuses multiple trajectories using relative pose constraints and robust noise models. Validation is conducted through two complex 3D surveys using the ATOM-ANT3D multi-camera fisheye mobile mapping system. Results demonstrate survey-grade accuracy comparable to traditional photogrammetry, with reduced computational time, advancing toward near real-time 3D mapping of challenging environments.

Traditional 3D surveying methods often fall short in complex spaces due to lack of mobility, time constraints and high risk. For this reason there is an actual demand for 3D data acquisition tools and methods, particularly suitable for complex and narrow environments, due to their capacity for efficiently capturing detailed and accurate spatial information, maybe also automatically. This study presents a novel approach for fusing 3D spatial data collected by two separate and independent mobile mapping systems: (1) ATOM-ANT3D and (2) MandEye. We propose an innovative fusion technique that combines visual and LiDAR data from asynchronous acquisitions, reducing the need for strict temporal and spatial synchronizations between the two systems. We compare the outputs of both systems before and after fusion, studying the individual limitations and highlighting the complementary benefits achieved by the proposed fusion framework. Results demonstrate improved accuracy of global alignment and spatial completeness of the final point clouds, proving the efficiency and flexibility of the proposed approach.

3D digitization has become an essential tool in cultural heritage documentation, offering unprecedented opportunities for preservation, analysis, and dissemination. Beyond only capturing 3D spatial geometry, the semantic enrichment of 3D models is rapidly evolving offering a more efficient interpretation and usage of 3D data. Traditionally, 3D semantic enrichment has relied on point cloud-based segmentation. However, 3D point cloud-based segmentation approaches can struggle with the efficient identification of small-scale, geometric elements, or visually ambiguous classes, limiting their applicability in such contexts. This study leverages the rich contextual and textural information of 2D imagery to detect challenging semantic categories, such as fine architectural elements (e.g., individual stone blocks) and material decay (e.g., material detachment and material loss), using deep learning-based 2D semantic segmentation techniques. These detections are then projected into 3D space through a 2D-to-3D semantic segmentation framework that couples V-SLAM and 3D results with the 2D predictions. The framework is evaluated on data acquired using the fish-eye multi-camera mobile mapping system ATOM-ANT3D in two challenging case study environments. Achieved results demonstrate a reliable level of accu-racy given the inherent complexity of targeted classes, enhancing the interpretability of 3D models by providing meaningful and met-rically interpreted objects classifications in 3D models. (Demonstration video: https://youtu.be/GidxhNS7ECc)