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.

翻译：本文提出一种基于计算机视觉的跟踪与融合算法，专用于在自然环境中运行的无人机三维打印云台系统。该系统通过将天际线和地平面作为参考，能够在具有挑战性的自然场景中稳健地稳定相机姿态。我们的主要贡献包括：(a) 从零训练轻量级ResNet-18骨干网络模型并部署至Jetson Nano平台，实现图像二值分割（天空与地面）；(b) 基于自然线索的几何假设，通过以天际线和地平面为参考实现鲁棒视觉跟踪；(c) 基于球面自适应粒子采样方法，灵活融合多传感器姿态信息。完整的算法流程已在包含Jetson及其他硬件组件的定制化云台上完成测试，并在真实建筑楼顶场景中开展实验验证。