一种面向微型飞行器门到门单目视觉导航的无地图深度学习框架
A Map-free Deep Learning-based Framework for Gate-to-Gate Monocular Visual Navigation aboard Miniaturized Aerial Vehicles
AI总结：
本文提出一种无地图单目视觉纳米无人机方案，结合轻量深度学习门检测与视觉伺服控制，以极低算力实现30 Hz闭环飞行，并在实地测试中成功高速穿越多个门。
摘要：
掌上大小的自主纳米无人机（即重量低于50克）最近进入了无人机竞速场景，其任务是在尽可能快地穿越门的同时避开障碍物。然而，与它们更大的同类（即千克级无人机）相比，纳米无人机的机载内存和计算能力低了三个数量级，因此需要更高效、更轻量的基于视觉的流程才能在比赛中获胜。本文提出了一种无地图的、基于视觉的（仅使用单目相机）自主纳米无人机，它将实时深度学习门检测前端与经典而优雅且有效的视觉伺服控制后端相结合，仅依赖机载资源。从两个最先进的微型深度学习模型出发，我们针对特定任务对其进行了适配，并在模拟器与真实世界混合训练之后，将其集成并部署到我们的纳米无人机上。我们性能最佳的流程每帧仅需2400万次乘加运算，实现了30 Hz的闭环控制性能，同时在我们约2万张真实世界图像数据集上达到了1.4像素的门检测均方根误差。实地实验凸显了我们的纳米无人机在4分钟内成功穿越15个门、从未坠毁并覆盖约100米总飞行距离的能力，峰值飞行速度为1.9 m/s。最后，为了强调我们系统的泛化能力，我们还在一个从未见过的环境中对其进行了测试，在该环境中它穿越门飞行了超过4分钟。
英文摘要：
Palm-sized autonomous nano-drones, i.e., sub-50g in weight, recently entered the drone racing scenario, where they are tasked to avoid obstacles and navigate as fast as possible through gates. However, in contrast with their bigger counterparts, i.e., kg-scale drones, nano-drones expose three orders of magnitude less onboard memory and compute power, demanding more efficient and lightweight vision-based pipelines to win the race. This work presents a map-free vision-based (using only a monocular camera) autonomous nano-drone that combines a real-time deep learning gate detection front-end with a classic yet elegant and effective visual servoing control back-end, only relying on onboard resources. Starting from two state-of-the-art tiny deep learning models, we adapt them for our specific task, and after a mixed simulator-real-world training, we integrate and deploy them aboard our nano-drone. Our best-performing pipeline costs of only 24M multiply-accumulate operations per frame, resulting in a closed-loop control performance of 30 Hz, while achieving a gate detection root mean square error of 1.4 pixels, on our ~20k real-world image dataset. In-field experiments highlight the capability of our nano-drone to successfully navigate through 15 gates in 4 min, never crashing and covering a total travel distance of ~100m, with a peak flight speed of 1.9 m/s. Finally, to stress the generalization capability of our system, we also test it in a never-seen-before environment, where it navigates through gates for more than 4 min.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
