The collaborative visual perception of multiple Unmanned Aerial Vehicles (UAVs) has increasingly become a research hotspot. Compared to a single UAV equipped with a short-baseline stereo camera, multi-UAV collaborative vision offers a wide and variable baseline, providing potential benefits in flexible and large-scale depth perception. In this paper, we propose the concept of a collaborative stereo camera, where the left and right cameras are mounted on two UAVs that share an overlapping FOV. Considering the dynamic flight of two UAVs in the real world, the FOV and relative pose of the left and right cameras are continuously changing. Compared to fixed-baseline stereo cameras, this aerial collaborative stereo system introduces two challenges, which are highly real-time requirements for dynamic cross-camera stereo feature association and relative pose estimation of left and right cameras. To address these challenges, we first propose a real-time dual-channel feature association algorithm with a guidance-prediction structure. Then, we propose a Relative Multi-State Constrained Kalman Filter (Rel-MSCKF) algorithm to estimate the relative pose by fusing co-visual features and UAVs' visual-inertial odometry (VIO). Extensive experiments are performed on the popular onboard computer NVIDIA NX. Results on the resource-constrained platform show that the real-time performance of the dual-channel feature association is significantly superior to traditional methods. The convergence of Rel-MSCKF is assessed under different initial baseline errors. In the end, we present a potential application of aerial collaborative stereo for remote mapping obstacles in urban scenarios. We hope this work can serve as a foundational study for more multi-UAV collaborative vision research. Online video: https://youtu.be/avxMuOf5Qcw

翻译：多无人机协同视觉感知日益成为研究热点。相较于搭载短基线立体相机的单无人机系统，多无人机协同视觉提供了宽基线且基线可变的优势，为灵活的大尺度深度感知带来了潜在效益。本文提出协同立体相机的概念，其左右相机分别搭载于两台具有重叠视野的无人机上。考虑到真实世界中两架无人机的动态飞行，左右相机的视野与相对位姿持续变化。与固定基线立体相机相比，这种空中协同立体系统引入了两大挑战：动态跨相机立体特征关联与左右相机相对位姿估计均需满足高度实时性要求。为应对这些挑战，我们首先提出一种具有引导-预测结构的实时双通道特征关联算法。随后，我们提出一种相对多状态约束卡尔曼滤波算法，通过融合共视特征与无人机的视觉惯性里程计来估计相对位姿。我们在主流机载计算机NVIDIA NX上进行了大量实验。资源受限平台上的结果表明，双通道特征关联算法的实时性能显著优于传统方法。我们评估了Rel-MSCKF在不同初始基线误差下的收敛性。最后，我们展示了空中协同立体视觉在城市场景中远程测绘障碍物的潜在应用。我们希望本工作能为更多多无人机协同视觉研究提供基础性探索。在线视频：https://youtu.be/avxMuOf5Qcw