Leveraging multiple cameras on Unmanned Aerial Vehicles (UAVs) to form a variable-baseline stereo camera for collaborative perception is highly promising. The critical steps include high-rate cross-camera feature association and frame-rate relative pose estimation of multiple UAVs. To accelerate the feature association rate to match the frame rate, we propose a dual-channel structure to decouple the time-consuming feature detection and match from the high-rate image stream. The novel design of periodic guidance and fast prediction effectively utilizes each image frame to achieve a frame-rate feature association. Real-world experiments are executed using SuperPoint and SuperGlue on the NVIDIA NX 8G platform with a 30 Hz image stream. Using single-channel SuperPoint and SuperGlue can only achieve 13 Hz feature association. The proposed dual-channel method can improve the rate of feature association from 13 Hz to 30 Hz, supporting the frame-rate requirement. To accommodate the proposed feature association, we develop a Multi-State Constrained Kalman Filter (MSCKF)-based relative pose estimator in the back-end by fusing the local odometry from two UAVs together with the measurements of common features. Experiments show that the dual-channel feature association improves the rate of visual observation and enhances the real-time performance of back-end estimator compared to the existing methods. Video - https://youtu.be/UBAR1iP0GPk Supplementary video - https://youtu.be/nPq8EpVzJZM

翻译：利用无人机（UAV）上的多台摄像机构成可变基线立体相机以实现协同感知具有广阔前景。关键步骤包括高速跨相机特征关联与多无人机的帧率级相对位姿估计。为加速特征关联速率以匹配帧率，我们提出双通道结构，将耗时的特征检测与匹配从高帧率图像流中解耦。周期性引导与快速预测的创新设计有效利用每帧图像实现帧率级特征关联。基于NVIDIA NX 8G平台搭载30 Hz图像流，使用SuperPoint与SuperGlue开展真实世界实验：单通道SuperPoint与SuperGlue仅能达到13 Hz特征关联，而所提双通道方法可将特征关联速率从13 Hz提升至30 Hz，满足帧率需求。为适配所提特征关联方法，我们在后端开发了基于多状态约束卡尔曼滤波（MSCKF）的相对位姿估计器，融合两架无人机的本地里程计与共视特征量测。实验表明，与现有方法相比，双通道特征关联提升了视觉观测速率并增强了后端估计器的实时性能。视频链接：https://youtu.be/UBAR1iP0GPk 补充视频：https://youtu.be/nPq8EpVzJZM