AIVIO：借助 AI 辅助视觉惯性里程计实现无人机的闭环、物体相对导航
AIVIO: Closed-loop, Object-relative Navigation of UAVs with AI-aided Visual Inertial Odometry
AI总结：
本文提出 AIVIO 无人机系统，用 RGB 相机与 IMU、合成数据训练并部署优化的深度学习位姿估计器实现实时物体相对闭环定位，并通过电线杆检查实飞验证。
摘要：
物体相对移动机器人导航对于多种任务都至关重要，例如自主关键基础设施检查，但它需要具备从原始传感器数据中提取感兴趣物体语义信息的能力。尽管基于深度学习（DL）的方法擅长从图像中推断语义物体信息，例如类别和相对六自由度（6-DoF）位姿，但这些方法计算需求很高，因此通常不适用于载荷受限的移动机器人。在这封信中，我们提出了一种具备实时能力的无人机（UAV）系统，用于物体相对的闭环导航；该系统采用由惯性测量单元（IMU）和 RGB 相机构成的最小传感器配置。系统利用一个基于深度学习的物体位姿估计器，将物体相对位姿测量与 IMU 数据融合，以执行物体相对定位；该估计器仅在合成数据上训练，并针对伴随板部署进行了优化。我们开展了多项真实世界实验，以验证系统在电线杆检查这一具有挑战性的用例中的性能。补充视频中展示了一次示例性的闭环飞行。
英文摘要：
Object-relative mobile robot navigation is essential for a variety of tasks, e.g. autonomous critical infrastructure inspection, but requires the capability to extract semantic information about the objects of interest from raw sensory data. While deep learning-based (DL) methods excel at inferring semantic object information from images, such as class and relative 6 degree of freedom (6-DoF) pose, they are computationally demanding and thus often not suitable for payload constrained mobile robots. In this letter we present a real-time capable unmanned aerial vehicle (UAV) system for object-relative, closed-loop navigation with a minimal sensor configuration consisting of an inertial measurement unit (IMU) and RGB camera. Utilizing a DL-based object pose estimator, solely trained on synthetic data and optimized for companion board deployment, the object-relative pose measurements are fused with the IMU data to perform object-relative localization. We conduct multiple real-world experiments to validate the performance of our system for the challenging use case of power pole inspection. An example closed-loop flight is presented in the supplementary video.
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