Revisiting Stereo Triangulation in UAV Distance Estimation

Distance estimation plays an important role for path planning and collision avoidance of swarm UAVs. However, the lack of annotated data seriously hinders related studies. In this work, we build and present a UAVDE dataset for UAV distance estimation, in which the distance between two UAVs is obtained by UWB sensors. During experiments, we observe that center-based stereo triangulation becomes unreliable in long-range UAV scenes. We show that this performance degradation is mainly caused by disparity errors introduced by practical UAV imaging conditions and amplified by long-range stereo geometry. To tackle this issue, we propose a novel position correction module, which predicts horizontal compensation offsets for the observed UAV centers under UWB distance supervision and applies them before stereo triangulation. Furthermore, to improve the robustness and generalization ability of position correction, we introduce a causal feature selection module into the position correction process. It adaptively selects correction-relevant causal feature channels and suppresses non-causal components caused by background interference, target appearance variation, and detection noise. We conduct extensive experiments on UAVDE. Our method achieves a significant performance improvement over a strong baseline (by reducing the relative difference from 49.4% to 8.6%), which demonstrates its effectiveness and superiority.