  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.
