UAST：无人机任意目标的统一主动搜索与跟踪
UAST: Unified Active Search and Tracking for Arbitrary Targets with UAVs
提出一种无需建图的RGB-D主动搜索与跟踪框架，其双分支感知模块和基于规则的搜索策略可被借鉴到无人机视觉导航、目标跟踪与主动感知任务中，特别是跟踪感知可见性损失和动态可行轨迹生成方法对视觉伺服控制有参考价值。
原文依据 a simple yet effective mapping-free framework that unifies active search and persistent tracking using only RGB-D observations
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论无人机在杂乱环境中对任意目标进行主动搜索与跟踪仍是一个极具挑战性的问题。现有方法要么构建复杂的模块化流程导致计算成本高昂，要么采用端到端控制器但往往难以泛化到不同目标和场景。此外，搜索与跟踪通常被分开处理，尽管二者之间存在强相互依赖关系。本文提出UAST，一个简单而有效的无建图框架，仅利用RGB-D观测统一主动搜索与持续跟踪。该系统将双分支感知模块与基于规则的点搜索策略相结合，自适应地在跟踪与基于搜索的恢复之间切换。一个轻量级控制网络直接从融合感知和无人机状态生成动态可行的轨迹。此外，我们引入了一种训练策略，包含精心设计的跟踪感知可见性损失和定制化的数据构建。在仿真和真实环境中的大量实验表明，与现有方法相比，我们的方法实现了更高的成功率、更稳定的长期跟踪和更快的目标搜索，同时保持高效率。代码见 https://github.com/qinliangql/UAST。
英文原文摘要 1336 字符
Active search and tracking of arbitrary targets by Unmanned Aerial Vehicles (UAVs) in cluttered environments remains a highly challenging problem. Existing methods either construct complex modular pipelines, leading to substantial computational costs, or adopt end-to-end controllers that often fail to generalize across different targets and scenes. Moreover, search and tracking are typically treated separately despite their strong interdependence. In this paper, we present UAST, a simple yet effective mapping-free framework that unifies active search and persistent tracking using only RGB-D observations. The proposed system couples a dual-branch perception module with a Rule-Based Point Search Policy that adaptively switches between tracking and search-based recovery. A lightweight control network generates dynamically feasible trajectories directly from fused perception and UAV states. Furthermore, we introduce a training strategy with an elaborated tracking-aware visibility loss and a tailored data construction. Extensive experiments in both simulated and real-world environments show that our approach achieves higher success rates, more stable long-term tracking, and faster target search compared with existing methods, while maintaining high efficiency. The code is available at https://github.com/qinliangql/UAST.