面向空中协作立体视觉：无人机的实时跨相机特征关联与相对位姿估计
Towards Aerial Collaborative Stereo: Real-Time Cross-Camera Feature Association and Relative Pose Estimation for UAVs
AI总结：
针对多无人机协作立体视觉的实时特征关联与相对位姿估计难题，提出带引导-预测结构的双通道特征关联算法和Rel-MSCKF算法，在NVIDIA NX上验证了实时性优势，为城市场景远程障测绘等应用提供研究基础。
摘要：
多无人机（UAV）的协作视觉感知日益成为研究热点。与搭载短基线立体相机的单架无人机相比，多无人机协作视觉具备宽且可变的基线，为灵活的大规模深度感知提供了潜在优势。本文提出协作立体相机的概念，其左右相机分别安装在两架具有重叠视场（FOV）的无人机上。考虑到现实中两架无人机的动态飞行，左右相机的视场和相对位姿会持续变化。与固定基线立体相机相比，这套空中协作立体系统带来了两项挑战：对动态跨相机立体特征关联的高实时性要求，以及左右相机的相对位姿估计需求。为应对这些挑战，我们首先提出一种具备引导-预测结构的实时双通道特征关联算法。随后，我们提出相对多状态约束卡尔曼滤波（Rel-MSCKF）算法，通过融合共视特征与无人机的视觉惯性里程计（VIO）来估计相对位姿。我们在主流机载计算机NVIDIA NX上开展了大量实验。在资源受限平台上的结果表明，双通道特征关联的实时性能显著优于传统方法。我们还在不同初始基线误差下评估了Rel-MSCKF的收敛性。最后，我们展示了空中协作立体视觉在城市场景远程障碍物测绘中的潜在应用。我们希望本研究能为更多多无人机协作视觉研究提供基础。在线视频：https://youtu.be/avxMuOf5Qcw
英文摘要：
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
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