Revisiting Stereo Triangulation in UAV Distance Estimation

• URL: http://arxiv.org/abs/2306.08939v2
• Date: Sat, 2 Dec 2023 13:19:59 GMT
• Title: Revisiting Stereo Triangulation in UAV Distance Estimation
• Authors: Jiafan Zhuang, Duan Yuan, Rihong Yan, Weixin Huang, Wenji Li, Zhun Fan
• Abstract summary: We build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. We propose a novel position correction module, which can directly predict the offset between the observed positions and the actual ones. We conduct extensive experiments on UAVDE, and our method can achieve a significant performance improvement over a strong baseline.
• Score: 5.656973345209692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distance estimation plays an important role for path planning and collision avoidance of swarm UAVs. However, the lack of annotated data seriously hinders the related studies. In this work, we build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. During experiments, we surprisingly observe that the stereo triangulation cannot stand for UAV scenes. The core reason is the position deviation issue due to long shooting distance and camera vibration, which is common in UAV scenes. To tackle this issue, we propose a novel position correction module, which can directly predict the offset between the observed positions and the actual ones and then perform compensation in stereo triangulation calculation. Besides, to further boost performance on hard samples, we propose a dynamic iterative correction mechanism, which is composed of multiple stacked PCMs and a gating mechanism to adaptively determine whether further correction is required according to the difficulty of data samples. We conduct extensive experiments on UAVDE, and our method can achieve a significant performance improvement over a strong baseline (by reducing the relative difference from 49.4% to 9.8%), which demonstrates its effectiveness and superiority. The code and dataset are available at https://github.com/duanyuan13/PCM.

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