  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
