Navigation is an essential capability for autonomous robots. In particular, visual navigation has been a major research topic in robotics because cameras are lightweight, power-efficient sensors that provide rich information on the environment. However, the main challenge of visual navigation is that it requires substantial computational power and memory for visual processing and storage of the results. As of yet, this has precluded its use on small, extremely resource-constrained robots such as lightweight drones. Inspired by the parsimony of natural intelligence, we propose an insect-inspired approach toward visual navigation that is specifically aimed at extremely resource-restricted robots. It is a route-following approach in which a robot’s outbound trajectory is stored as a collection of highly compressed panoramic images together with their spatial relationships as measured with odometry. During the inbound journey, the robot uses a combination of odometry and visual homing to return to the stored locations, with visual homing preventing the buildup of odometric drift. A main advancement of the proposed strategy is that the number of stored compressed images is minimized by spacing them apart as far as the accuracy of odometry allows. To demonstrate the suitability for small systems, we implemented the strategy on a tiny 56-gram drone. The drone could successfully follow routes up to 100 meters with a trajectory representation that consumed less than 20 bytes per meter. The presented method forms a substantial step toward the autonomous visual navigation of tiny robots, facilitating their more widespread application.

中文翻译：

---

小型自主机器人的视觉路线跟踪


导航是自主机器人的一项基本功能。特别是，视觉导航一直是机器人技术的一个主要研究课题，因为相机是轻量级、节能的传感器，可以提供丰富的环境信息。然而，视觉导航的主要挑战是它需要大量的计算能力和内存来进行视觉处理和结果存储。到目前为止，这已经排除了它在小型、资源极其有限的机器人（例如轻型无人机）上的使用。受自然智能简约性的启发，我们提出了一种受昆虫启发的视觉导航方法，专门针对资源极其有限的机器人。它是一种路线跟踪方法，其中机器人的出站轨迹存储为高度压缩的全景图像的集合以及通过里程计测量的空间关系。在回程旅程中，机器人结合使用里程计和视觉归航来返回到存储位置，视觉归航可防止里程计漂移的累积。所提出策略的一个主要进步是，通过在里程计精度允许的范围内将压缩图像间隔开来最大程度地减少存储的压缩图像的数量。为了证明对小型系统的适用性，我们在一架 56 克的微型无人机上实施了该策略。该无人机可以成功地沿着长达 100 米的路线飞行，其轨迹表示每米消耗的字节数不到 20 个字节。所提出的方法向微型机器人的自主视觉导航迈出了实质性的一步，促进了其更广泛的应用。