用语义分割指导无人机避障导航，提升复杂环境定位可靠性。
Toward Integrating Semantic-aware Path Planning and Reliable Localization for UAV Operations
- 基于单目相机的语义分割，识别无纹理区域并规划绕行路径。
- 实时优化关键帧选择，处理时间降低，定位成功率显著提升。
- 适合复杂城市或水域环境下的无人机自主飞行任务。
定位是无人机系统中最关键的任务之一，直接影响整体性能，可通过多种传感器实现，应用于搜救、目标追踪、建筑监测等场景。然而，在复杂环境中，无人机可能因信号丢失导致定位失效。本文提出一种融合语义分割信息的高效路径规划系统，利用单目相机识别无纹理区域（如湖泊、海洋、高层建筑）并规划绕行路径。设计了实时语义分割架构与新型关键帧决策流程，基于像素分布优化图像输入，减少处理时间。采用基于动态窗口法（DWA）的分层规划器，结合代价地图实现高效路径规划。系统在Unity中搭建的逼真仿真环境中实现，模型参数对齐。定性与定量评估表明，该方法在挑战性环境下显著提升了无人机定位的可靠性与效率。
原文摘要 · Abstract (English)
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.
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