Abstract Teach and Repeat (T&R) refers to the technology that allows a robot to autonomously follow a previously traversed route, in a natural scene and using only its onboard sensors. In this paper we present a Visual-Inertial Teach and Repeat (VI-T&R) algorithm that uses stereo and inertial data and targets Unmanned Aerial Vehicles with limited on-board computational resources. We propose a tightly-coupled relative formulation of the visual-inertial constraints that is tailored to the T&R application. In order to achieve real-time operation on limited hardware, we reduce the problem to motion-only visual-inertial Bundle Adjustment. In the repeat stage, we detail how to generate a trajectory and smoothly follow it with a constantly changing relative frame. The proposed method is validated in simulated environments, using real sensor data from the public EuRoC dataset, and using our own robotic setup and closed-loop control. Our experimental results demonstrate high accuracy and real-time performance both on a standard desktop system and on a low-cost Odroid X-U4 embedded computer.

中文翻译：

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视觉惯性示教和重复

摘要 示教与重复 (T&R) 是指允许机器人在自然场景中仅使用其机载传感器自主遵循先前穿过的路线的技术。在本文中，我们提出了一种视觉惯性示教和重复 (VI-T&R) 算法，该算法使用立体和惯性数据并针对机载计算资源有限的无人机。我们提出了一种针对 T&R 应用程序定制的视觉惯性约束的紧密耦合相对公式。为了在有限的硬件上实现实时操作，我们将问题简化为仅运动的视觉惯性束调整。在重复阶段，我们详细说明了如何生成轨迹并在不断变化的相对框架下平滑地跟随它。所提出的方法在模拟环境中得到验证，使用来自公共 EUROC 数据集的真实传感器数据，并使用我们自己的机器人设置和闭环控制。我们的实验结果证明了在标准桌面系统和低成本 Odroid X-U4 嵌入式计算机上的高精度和实时性能。