基于标签的视觉里程计估计用于室内无人机定位
Tag-based Visual Odometry Estimation for Indoor UAVs Localization
AI总结：
本文针对室内GNSS拒止环境下的无人机定位问题，提出一种基于密集异构标签地图的视觉里程计方法，通过分层标签选择、异常值剔除和多标签融合提升定位精度。
摘要：
无人机平台所提供的灵活性和多功能性，在工业应用中的充分开发仍然遇到障碍，原因在于其室内使用的局限性。在这一方面，一个重大挑战是找到一种可靠且成本效益高的方法，在GNSS拒止环境中对飞行器进行定位。本文聚焦于基于视觉的定位范式：通过利用密集且尺寸异构的标签地图所提供的潜力，实现了无人机位置和姿态估计的高精度。具体而言，我们提出了一种高效的视觉里程计流程，重点包括分层标签选择、异常值剔除和多标签估计融合，以促进视觉-惯性协调。实验结果表明，与现有最先进技术相比，所提出的定位架构是有效的。
英文摘要：
The agility and versatility offered by UAV platforms still encounter obstacles for full exploitation in industrial applications due to their indoor usage limitations. A significant challenge in this sense is finding a reliable and cost-effective way to localize aerial vehicles in a GNSS-denied environment. In this paper, we focus on the visual-based positioning paradigm: high accuracy in UAVs position and orientation estimation is achieved by leveraging the potentials offered by a dense and size-heterogenous map of tags. In detail, we propose an efficient visual odometry procedure focusing on hierarchical tags selection, outliers removal, and multi-tag estimation fusion, to facilitate the visual-inertial reconciliation. Experimental results show the validity of the proposed localization architecture as compared to the state of the art.
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。
