Run-of-mine fragmentation is an important aspect of mine productivity optimisation, as it affects all mine-to-mill processes. In this study, a 3D rock fragmentation measurement (3DFM) system is proposed that can surmount the limitations of conventional 2D photo-based rock fragmentation measurement methods. To validate the proposed 3DFM performance, a laboratory-based comparison study was conducted using 100 randomly collected rock fragments. Conventional method exhibits relatively low and diffused results than the 3DFM. The proposed 3DFM can be considered as a new particle size distribution measurement method that should efficiently aid in improving mine productivity.

This work proposes an image-alignment method for multi-modal images (e.g., synthetic aperture radar (SAR) and optical satellite images) using an image-feature-based keypoint-matching algorithm. In applying the matching algorithm to multi-modal images, common features need to be obtained at the corresponding positions. However, the appearances of features among images are different. We solve this issue by translating the appearance of one modal image to the other using generative adversarial networks (GANs). In this work, we attempt to generate optical images from SAR images as a way to extract common features. Through an experiment, we confirm that the proposed method can estimate accurate correspondences between SAR and optical images.

Simultaneous Localization and Mapping (SLAM) forms the foundation of vehicle localization in autonomous driving. Utilizing high-precision 3D scene maps as prior information in vehicle localization greatly assists in the navigation of autonomous vehicles within large-scale 3D scene models. However, generating high-precision maps is complex and costly, posing challenges to commercialization. As a result, a global localization system that employs low-precision, city-scale 3D scene maps reconstructed by unmanned aerial vehicles (UAVs) is proposed to optimize visual positioning for vehicles. To address the discrepancies in image information caused by differing aerial and ground perspectives, this paper introduces a wall complementarity algorithm based on the geometric structure of buildings to refine the city-scale 3D scene. A 3D-to-3D feature registration algorithm is developed to determine vehicle location by integrating the optimized city-scale 3D scene with the local scene generated by an onboard stereo camera. Through simulation experiments conducted in a computer graphics (CG) simulator, the results indicate that utilizing a completed low-precision scene model enables achieving a vehicle localization accuracy with an average error of 3.91 m, which is close to the 3.27 m error obtained using the high-precision map. This validates the effectiveness of the proposed algorithm. The system demonstrates the feasibility of utilizing low-precision city-scale 3D scene maps generated by unmanned aerial vehicles (UAVs) for vehicle localization in large-scale scenes.

Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data.