Conventional or learning-based 3D reconstruction methods from images have clearly shown their potential for 3D heritage documentation. Nevertheless, Neural Radiance Field (NeRF) approaches are recently revolutionising the way a scene can be rendered or reconstructed in 3D from a set of oriented images. Therefore the paper wants to review some of the last NeRF methods applied to various cultural heritage datasets collected with smartphone videos, touristic approaches or reflex cameras. Firstly several NeRF methods are evaluated. It turned out that Instant-NGP and Nerfacto methods achieved the best outcomes, outperforming all other methods significantly. Successively qualitative and quantitative analyses are performed on various datasets, revealing the good performances of NeRF methods, in particular for areas with uniform texture or shining surfaces, as well as for small datasets of lost artefacts. This is for sure opening new frontiers for 3D documentation, visualization and communication purposes of digital heritage.

Usually, substantial part of a mine haulage system is based on belt conveyors. Reliability of such system is significant in terms of mining operation continuity and profitability. Numerous methods for conveyor belt monitoring have been developed, although many of them require physical presence of the monitoring staff in the dangerous environment. In this paper, a remote sensing method for assessing a conveyor belt condition using the Terrestrial Laser Scanner (TLS) system has been described. For this purpose a methodology of semi-automatic processing of point cloud data for obtaining the belt geometry has been developed. The sample data has been collected in a test laboratory and processed with the proposed algorithms. Damaged belt surface areas have been successfully identified and edge defects were investigated. The proposed non-destructive testing methodology has been found to be suitable for monitoring the general condition of the conveyor belt and could be exceptionally successful and cost-effective if combined with an unmanned, robotic inspection system.

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

Mining industry faces new technological and economic challenges which need to be overcome in order to raise it to a new technological level in accordance with the ideas of Industry 4.0. Mining companies are searching for new possibilities of optimizing and automating processes, as well as for using digital technology and modern computer software to aid technological processes. Every stage of deposit management requires mining engineers, geologists, surveyors, and environment protection specialists who are involved in acquiring, storing, processing, and sharing data related to the parameters describing the deposit, its exploitation and the environment. These data include inter alia: geometries of the deposit, of the excavations, of the overburden and of the mined mineral, borders of the support pillars and of the buffer zones, mining advancements with respect to the set borders, effects of mining activities on the ground surface, documentation of landslide hazards and of the impact of mining operations on the selected elements of the environment. Therefore, over the life cycle of a deposit, modern digital technological solutions should be implemented in order to automate the processes of acquiring, sharing, processing and analyzing data related to deposit management. In accordance with this idea, the article describes the results of a measurement experiment performed in the Mikoszów open-pit granite mine (Lower Silesia, SW Poland) with the use of mobile LiDAR systems. The technology combines active sensors with automatic and global navigation system synchronized on a mobile platform in order to generate an accurate and precise geospatial 3D cloud of points.

The use of mobile mapping technologies (MMT) has become increasingly popular across various applications such as forestry, cultural heritage, mining, and civil engineering. While Simultaneous Localization and Mapping (SLAM) algorithms have greatly improved in recent years with regards to accuracy, robustness, and cooperativity, it is important to understand the limitations and strengths of each metrological measurement method to ensure the provision of 3D data of appropriate quality for the selected application. In this study, we perform a comparative analysis of three LiDAR-based handheld mobile mapping systems with survey-grade reference point clouds in a challenging test area of a partially collapsed underground tunnel. We investigate various aspects of 3D data quality, including accuracy and completeness, and present an improved method for 3D data completeness assessment aimed at evaluating SLAM-derived point clouds. The results demonstrate unique and diverse strengths and shortcomings of the tested mapping systems, which provide valuable guidelines for selecting an appropriate system for subterranean applications.

Locating an inspection robot is an essential task for inspection missions and spatial data acquisition. Giving a spatial reference to measurements, especially those concerning environmental parameters, e.g., gas concentrations may make them more valuable by enabling more insightful analyses. Thus, an accurate estimation of sensor position and orientation is a significant topic in mobile measurement systems used in robotics, remote sensing, or autonomous vehicles. Those systems often work in urban or underground conditions, which are lowering or disabling the possibility of using Global Navigation Satellite Systems (GNSS) for this purpose. Alternative solutions vary significantly in sensor configuration requirements, positioning accuracy, and computational complexity. The selection of the optimal solution is difficult. The focus here is put on the assessment, using the criterion of the positioning accuracy of the mobile robot with no use of GNSS signals. Automated geodetic surveying equipment is utilized for acquiring precise ground truth data of the robot’s movement. The results obtained, with the use of several methods, compared: Wheel odometry, inertial measurement-based dead-reckoning, visual odometry, and trilateration of ultra-wideband signals. The suitability, pros, and cons of each method are discussed in the context of their application in autonomous robotic systems, operating in an underground mine environment.