神经几何隧道穿越：无定位无人机飞行与倾斜激光雷达
Neural-Geometric Tunnel Traversal: Localization-free UAV Flight with Tilted LiDARs
AI总结：
本文提出一种基于几何和机器学习算法的无人机导航方法，利用倾斜激光雷达信息实现无定位的隧道飞行，通过深度神经网络确定无人机姿态并计算安全路径。
摘要：
本文提出了一种基于几何和机器学习算法的无人机导航方法，利用倾斜激光雷达信息实现无定位的隧道飞行，通过深度神经网络确定无人机姿态并计算安全路径。
英文摘要：
Navigation of UAVs in challenging environments like tunnels or mines, where it is not possible to use GNSS methods to self-localize, illumination may be uneven or nonexistent, and wall features are likely to be scarce, is a complex task, especially if the navigation has to be done at high speed. In this paper we propose a novel proof-of-concept navigation technique for UAVs based on the use of LiDAR information through the joint use of geometric and machine-learning algorithms. The perceived information is processed by a deep neural network to establish the yaw of the UAV with respect to the tunnel's longitudinal axis, in order to adjust the direction of navigation. Additionally, a geometric method is used to compute the safest location inside the tunnel (i.e. the one that maximizes the distance to the closest obstacle). This information proves to be sufficient for simple yet effective navigation in straight and curved tunnels.
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。
