Transformer-Based Network for Robust 3D Industrial Environment Understanding in Autonomous UAV Systems
Autonomous navigation of unmanned aerial vehicles (UAVs) in unstructured industrial environments remains challenging due to irregular geometry, dynamic obstacles and sensor uncertainty. Classical Simultaneous Localization and Mapping (SLAM) systems, though geometrically consistent, often fail under poor initialization, textureless areas or reflective surfaces. To overcome these issues, this work proposes a hybrid transformer-geometric framework that fuses learned scene priors with keyframe-based SLAM.