UAV Position Estimation Using a LiDAR-based 3D Object Detection Method
Accurate relative localization of unmanned aerial vehicles (UAVs) with respect to unmanned ground vehicles (UGVs) remains challenging in GPS-denied environments. Method: This paper proposes a real-time 3D detection and 6-DoF relative pose estimation method leveraging a UGV-mounted LiDAR and the PointPillars deep learning architecture—specifically, pillar-based voxelization followed by 2D CNN processing. To our knowledge, this is the first application of PointPillars to UAV relative localization, replacing conventional pipelines involving point cloud segmentation, Euclidean clustering, and heuristic rules. The approach performs end-to-end point cloud processing for 3D UAV detection and integrates geometric constraints to solve for full six-degree-of-freedom relative pose. Contribution/Results: Evaluated in real-world GPS-denied scenarios, the method achieves a 37.2% improvement in localization accuracy over baseline approaches, with markedly enhanced robustness and stability. Validation against ground truth confirms its effectiveness. This work establishes a scalable, lightweight deep learning paradigm for multi-agent collaborative perception and localization.