Localization is one of the most crucial tasks for Unmanned Aerial Vehicle systems (UAVs) directly impacting overall performance, which can be achieved with various sensors and applied to numerous tasks related to search and rescue operations, object tracking, construction, etc. However, due to the negative effects of challenging environments, UAVs may lose signals for localization. In this paper, we present an effective path-planning system leveraging semantic segmentation information to navigate around texture-less and problematic areas like lakes, oceans, and high-rise buildings using a monocular camera. We introduce a real-time semantic segmentation architecture and a novel keyframe decision pipeline to optimize image inputs based on pixel distribution, reducing processing time. A hierarchical planner based on the Dynamic Window Approach (DWA) algorithm, integrated with a cost map, is designed to facilitate efficient path planning. The system is implemented in a photo-realistic simulation environment using Unity, aligning with segmentation model parameters. Comprehensive qualitative and quantitative evaluations validate the effectiveness of our approach, showing significant improvements in the reliability and efficiency of UAV localization in challenging environments.

翻译：定位是无人机系统最为关键的任务之一，其直接影响整体性能，可通过多种传感器实现，并应用于搜索救援、目标跟踪、建筑施工等众多任务。然而，由于复杂环境的不利影响，无人机可能丢失定位信号。本文提出一种有效的路径规划系统，利用语义分割信息，通过单目相机引导无人机避开湖泊、海洋和高层建筑等纹理缺失或问题区域。我们引入了一种实时语义分割架构和一种新颖的关键帧决策流程，基于像素分布优化图像输入，从而减少处理时间。设计了一种基于动态窗口法算法的分层规划器，并结合代价地图，以实现高效路径规划。该系统在Unity构建的逼真仿真环境中实现，并与分割模型参数对齐。全面的定性与定量评估验证了本方法的有效性，表明其在复杂环境下显著提升了无人机定位的可靠性与效率。