  In this paper, we present a Computer Vision (CV) based tracking and fusion
algorithm, dedicated to a 3D printed gimbal system on drones operating in
nature. The whole gimbal system can stabilize the camera orientation robustly
in a challenging nature scenario by using skyline and ground plane as
references. Our main contributions are the following: a) a light-weight
Resnet-18 backbone network model was trained from scratch, and deployed onto
the Jetson Nano platform to segment the image into binary parts (ground and
sky); b) our geometry assumption from nature cues delivers the potential for
robust visual tracking by using the skyline and ground plane as a reference; c)
a spherical surface-based adaptive particle sampling, can fuse orientation from
multiple sensor sources flexibly. The whole algorithm pipeline is tested on our
customized gimbal module including Jetson and other hardware components. The
experiments were performed on top of a building in the real landscape.
