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：本文提出了一种基于计算机视觉（CV）的跟踪与融合算法，专门用于在自然环境中运行的无人机三维打印云台系统。该云台系统通过以天际线和地平面为参考，能够在复杂的自然场景中稳健地稳定相机姿态。我们的主要贡献如下：a）从头训练了一个轻量级Resnet-18骨干网络模型，并部署在Jetson Nano平台上，将图像分割为二值部分（地面和天空）；b）基于自然线索的几何假设，通过利用天际线和地平面作为参考，实现了稳健的视觉跟踪潜力；c）基于球面的自适应粒子采样，能够灵活融合来自多传感器源的姿态信息。整个算法流程在我们定制的云台模块（包括Jetson及其他硬件组件）上进行了测试。实验在真实景观的建筑物顶部展开。