The Euclidean Signed Distance Field (ESDF) is widely used in visibility evaluation to prevent occlusions and collisions during tracking. However, frequent ESDF updates introduce considerable computational overhead. To address this issue, we propose Eva-Tracker, a visibility-aware trajectory planning framework for aerial tracking that eliminates ESDF updates and incorporates a recovery-capable path generation method for target reacquisition. First, we design a target trajectory prediction method and a visibility-aware initial path generation algorithm that maintain an appropriate observation distance, avoid occlusions, and enable rapid replanning to reacquire the target when it is lost. Then, we propose the Field of View ESDF (FoV-ESDF), a precomputed ESDF tailored to the tracker's field of view, enabling rapid visibility evaluation without requiring updates. Finally, we optimize the trajectory using differentiable FoV-ESDF-based objectives to ensure continuous visibility throughout the tracking process. Extensive simulations and real-world experiments demonstrate that our approach delivers more robust tracking results with lower computational effort than existing state-of-the-art methods. The source code is available at https://github.com/Yue-0/Eva-Tracker.

翻译：欧几里得符号距离场（ESDF）在可见性评估中被广泛用于防止跟踪过程中的遮挡与碰撞。然而，频繁的ESDF更新会引入显著的计算开销。为解决这一问题，我们提出了Eva-Tracker，一种用于空中跟踪的可见性感知轨迹规划框架，该框架消除了ESDF更新，并集成了具备恢复能力的目标重捕获路径生成方法。首先，我们设计了一种目标轨迹预测方法和一种可见性感知的初始路径生成算法，以保持适当的观测距离、避免遮挡，并在目标丢失时能够快速重新规划以重获目标。接着，我们提出了视场ESDF（FoV-ESDF），这是一种针对跟踪器视场预计算的ESDF，无需更新即可实现快速的可见性评估。最后，我们利用基于可微FoV-ESDF的目标函数对轨迹进行优化，以确保在整个跟踪过程中保持连续的可见性。大量的仿真与真实世界实验表明，与现有最先进方法相比，我们的方法能以更低的计算代价获得更鲁棒的跟踪结果。源代码发布于 https://github.com/Yue-0/Eva-Tracker。