论文标题
基于自适应采样的粒子滤波器，用于野外视觉惯性阳性
Adaptive Sampling-based Particle Filter for Visual-inertial Gimbal in the Wild
论文作者
论文摘要
在本文中，我们介绍了基于计算机视觉（CV）的跟踪和融合算法，该算法专门针对自然界运行的无人机上的3D打印kimbal系统。通过使用天际线和接地平面作为参考，整个世界齿轮系统可以在充满挑战性的自然情况下稳健地稳定相机方向。我们的主要贡献是：a）从Scratch训练了轻重的RESNET-18骨干网络模型，并部署到Jetson Nano平台上，将图像分割为二进制零件（地面和天空）； b）通过使用天际线和接地平面作为参考，我们来自自然线索的几何假设为可靠的视觉跟踪提供了潜力； c）基于球形表面的自适应颗粒采样，可以灵活地从多个传感器来源融合方向。整个算法管道将在我们的定制妇女模块上测试，包括Jetson和其他硬件组件。实验是在真正的景观中的建筑物顶部进行的。
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.
