308 reference across GNSS positioning, geological modeling, and AR rendering, the framework supports stable spatial alignment required for bench-scale visualization and grade-control interpretation. 2.4. 3D Ore-Grade Model Preparation and Representation The 3D ore-grade model used in this study was developed using Maptek Vulcan software based on existing geological and grade control datasets. Rather than employing dense voxel-based block models, the system utilizes polygon-based grade representations that reflect operational grade control boundaries commonly used at the bench level. Each polygon represents a discrete ore classification zone, including lowgrade, medium-grade, high-grade, and waste material. This representation reduces computational complexity and rendering load while maintaining sufficient spatial resolution for real-time field visualization. Polygonbased models are particularly well suited to AR applications, as they enable faster rendering and clearer visual interpretation on mobile hardware. All polygon models were generated in the same projected coordinate system as the GNSS data to ensure spatial consistency. The models were exported from Maptek Vulcan and changed to the Wavefront (OBJ) format in CloudCompare, preserving geometric accuracy and spatial positioning. Prior to integration into the AR environment, polygon meshes were optimized and standardized color codes were assigned to each grade category to enhance visual clarity and reduce rendering overhead. 2.5. Visualization and Synchronization Strategy The visualization framework enables real-time spatial correspondence between the digital ore-grade model and the physical bench environment. A Unity-based augmented-reality application integrated with the XREAL SDK manages coordinate alignment, rendering, and user visualization. Real-time positional updates derived from the RTK GNSS subsystem are transformed into the projected mine coordinate frame and applied to the virtual camera pose, while the ore-grade model remains fixed in global space. This configuration produces the perception of grade domains embedded within the bench as the operator moves. To maintain spatial stability during motion and temporary GNSS degradation, the system applies multi-sensor pose tracking combining RTK GNSS positioning, IMU-based motion sensing, and stereo camera Visual SLAM. GNSS provides absolute spatial reference, while visual-inertial tracking ensures short-term continuity and mitigates drift. Rendering is implemented using Unity’s Universal Render Pipeline with pose prediction and reprojection provided by XREAL SDK. 2.6. Field Implementation, Communication, and Power Configuration The communication bridge and hardware configuration described in this section are specific to the research prototype developed in this study and are not representative of production augmented-reality mining system architectures. Production deployments utilize alternative integration approaches suited to operational mining environments, and details of such architectures are commercially sensitive where they are subject to patent protection. The system is designed for practical deployment in open-pit mining environments where fixed infrastructure and network connectivity may be limited. A hybrid communication architecture is implemented to balance transmission range, reliability, and energy efficiency. Differential correction data between the RTK GNSS base station and rover are transmitted using long-range communication methods, removing dependence on external network infrastructure, while short-range wireless links enable real-time coordinate transfer between system components. The smart-glass display is directly interfaced with the mobile device through a high-bandwidth wired connection, enabling simultaneous power delivery and realtime visual rendering with minimal cabling complexity during field operation. Portable battery packs supply power to all mobile components, allowing several hours of continuous operation without external power sources.
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