Figure 5. Statistical comparison of positioning error under different correction strategies (no correction, IMU correction, and cumulative position correction) for solo GNSS positioning and RTK-GNSS. Box plots indicate median, interquartile range, and distribution of positional error. Table 3. Quantitative positioning accuracy obtained under different correction strategies for solo GNSS positioning and RTK-GNSS. No correction IMU Correction Cumulative position correction Solo positioning 1.54m 3.35m 2.00m RTK-GNSS 0.83m 2.67m 0.46m Latency between GNSS acquisition and AR rendering remained within limits acceptable for walking-speed bench inspection, supporting stable visualization without perceptible delay. These characteristics indicate that the proposed system is well suited to short-range grade verification, supports grade-control interpretation, enhances situational awareness, enables visual verification of ore-waste boundaries. Overall, the results demonstrate that polygon-based ore modeling combined with RTKGNSS-anchored augmented reality can deliver spatially coherent, operationally meaningful grade visualization at the bench scale. This establishes a technical foundation for future quantitative validation, extended field deployment, and integration with real-time digital-twin mining workflows. 4. CONCLUSION
RkJQdWJsaXNoZXIy MTM0Mzk2