ACCURATE REAL-TIME TRACKING IN GEOLOGY: A DATA-DRIVEN APPROACH

Accurately tracking the position of machinery like tunnel borers or robots in underground environments is a major challenge. Standard GPS fails in these settings, and sensor systems like inertial navigation units accumulate large positioning errors over time. This paper presents a novel, data-driven solution to this problem. The core of our system is a powerful data processing framework that fuses these vast, real-time sensor datasets. Using a machine learning model, we correct the drift in the motion sensors, enabling highly accurate, real-time geological positioning.