Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, are demonstrated to static local optima and two-dimensional exploration. To address these challenges, this paper introduces GO-FEAP (Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning), aiming to achieve efficient and complete three-dimensional exploration. Frontier-Omission-Aware Exploration module presented in this work takes into account multiple pivotal factors, encompassing frontier distance, nearby frontier count, frontier duration, and frontier categorization, for a comprehensive assessment of frontier importance. Furthermore, to tackle scenarios with substantial vertical variations, we introduce the Altitude-Stratified Planning strategy, which stratifies the three-dimensional space based on altitude, conducting global-local planning for each stratum. The objective of global planning is to identify the most optimal frontier for exploration, followed by viewpoint selection and local path optimization based on frontier type, ultimately generating dynamically feasible three-dimensional spatial exploration trajectories. We present extensive benchmark and real-world tests, in which our method completes the exploration tasks with unprecedented completeness compared to state-of-the-art approaches.

翻译：自主探测是无人机各类应用中的基础问题，然而现有方法常局限于静态局部最优和二维探测场景。为解决上述挑战，本文提出GO-FEAP（基于边界遗漏感知探测与高度分层规划的全局最优无人机规划器），旨在实现高效完整的三维探测。本文提出的边界遗漏感知探测模块综合考虑边界距离、邻近边界数量、边界持续时间及边界分类等多个关键因素，对边界重要性进行综合评估。此外，为应对垂直方向存在显著差异的场景，我们引入高度分层规划策略，该策略依据高度对三维空间进行分层，并针对每层执行全局-局部规划。全局规划旨在识别最优探测边界，随后根据边界类型进行视点选择与局部路径优化，最终生成具备动态可行性的三维空间探测轨迹。我们开展了广泛的基准测试与真实环境实验，相较于当前最先进方法，本方法以空前的完整性完成了探测任务。