无人机追踪无需频繁更新距离场，靠预计算视野场实现高效避障与目标重捕。
Eva-Tracker: ESDF-update-free, Visibility-aware Planning with Target Reacquisition for Robust Aerial Tracking
- 用预计算的视野距离场替代频繁更新，降低计算开销。
- 设计可快速重捕目标的路径生成策略，提升追踪鲁棒性。
- 适合需要低延迟、高可靠性的无人机视觉追踪场景。
欧氏有符号距离场（ESDF）广泛用于评估可见性，以避免追踪过程中的遮挡和碰撞。然而，频繁更新ESDF带来显著计算开销。为此，我们提出Eva-Tracker，一种免更新ESDF的可见性感知轨迹规划框架，包含支持目标重捕的路径生成方法。首先，设计目标轨迹预测与可见性感知初始路径生成算法，保持合适观测距离，规避遮挡，并在目标丢失时快速重规划以重新获取目标。其次，提出视场有符号距离场（FoV-ESDF），一种针对追踪器视场预计算的ESDF，实现无需更新即可快速评估可见性。最后，利用可微分的FoV-ESDF目标优化轨迹，确保全程连续可见。大量仿真与真实世界实验表明，本方法在计算开销更低的前提下，实现了比现有最先进方法更鲁棒的追踪效果。源代码已开源：https://github.com/Yue-0/Eva-Tracker。
原文摘要 · Abstract (English)
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.
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