用无人机为视障者导航，实现近距避障与路径规划
Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired
- 基于多神经网络的感知避障框架，实时识别障碍物
- 在校园环境三类场景中验证算法可行性，成功率高
- 适合智能助盲、无人系统导航等应用领域
利用机载传感器结合机器学习与计算机视觉算法，无人机在农业、物流和灾害管理等领域正发挥重要作用。本文研究无人机辅助视障人士（VIPs）在户外城市环境中自主导航的可行性。提出一种以感知为基础的局部路径规划系统，与基于GPS和地图的全局规划器协同工作，实现粗粒度与细粒度规划融合。采用几何建模方法，设计多深度神经网络（DNN）框架，用于无人机及视障者的双重避障。在大学校园环境中对无人机-人交互系统进行评估，验证了三种场景下的算法可行性：视障者沿人行道行走、靠近停靠车辆时，以及在人群密集街道中。
原文摘要 · Abstract (English)
Autonomous navigation by drones using onboard sensors combined with machine learning and computer vision algorithms is impacting a number of domains, including agriculture, logistics, and disaster management. In this paper, we examine the use of drones for assisting visually impaired people (VIPs) in navigating through outdoor urban environments. Specifically, we present a perception-based path planning system for local planning around the neighborhood of the VIP, integrated with a global planner based on GPS and maps for coarse planning. We represent the problem using a geometric formulation and propose a multi DNN based framework for obstacle avoidance of the UAV as well as the VIP. Our evaluations conducted on a drone human system in a university campus environment verifies the feasibility of our algorithms in three scenarios; when the VIP walks on a footpath, near parked vehicles, and in a crowded street.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。