用深度学习提取关键点，让无人机靠视觉精准导航。
Deep Visual Servoing of an Aerial Robot Using Keypoint Feature Extraction
- 用卷积网络从单目相机图像中自动提取关键特征。
- 在遮挡、光照变化等干扰下仍能稳定控制无人机运动。
- 基于真实物理仿真测试，适合实际无人机视觉控制研究者。
本文针对基于图像的视觉伺服（IBVS）问题，提出一种基于深度学习的关键点检测方法，用于空中机器人控制。采用安装在平台上的单目RGB相机采集视觉数据，并通过卷积神经网络（CNN）提取特征作为伺服任务的输入。该方法不仅避免了传统视觉伺服对人工标记的依赖，还显著提升了对遮挡、光照变化、杂乱背景及环境变动等不利因素的鲁棒性，从而扩展了感知引导运动控制在无人机中的应用范围。此外，本文通过大量基于物理引擎的ROS Gazebo仿真评估该方法的有效性，区别于多数仅依赖无物理模拟的研究。演示视频可访问 https://youtu.be/Dd2Her8Ly-E。
原文摘要 · Abstract (English)
The problem of image-based visual servoing (IBVS) of an aerial robot using deep-learning-based keypoint detection is addressed in this article. A monocular RGB camera mounted on the platform is utilized to collect the visual data. A convolutional neural network (CNN) is then employed to extract the features serving as the visual data for the servoing task. This paper contributes to the field by circumventing not only the challenge stemming from the need for man-made marker detection in conventional visual servoing techniques, but also enhancing the robustness against undesirable factors including occlusion, varying illumination, clutter, and background changes, thereby broadening the applicability of perception-guided motion control tasks in aerial robots. Additionally, extensive physics-based ROS Gazebo simulations are conducted to assess the effectiveness of this method, in contrast to many existing studies that rely solely on physics-less simulations. A demonstration video is available at https://youtu.be/Dd2Her8Ly-E.
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