PIONEER: Panoramic Imagined Occupancy-guided Active Mapping for Efficient Aerial Exploration

Active mapping seeks informative observations for efficient aerial exploration and reconstruction, yet perspective observations couple viewpoint utility to camera positions and orientations, increasing the cost of explicit search over candidate poses. Panoramic vision instead provide nearly complete angular coverage and enable position-centric planning. However, exploration in large environments still requires anticipating occluded geometry and allocating computation across candidate spatial trajectories. We present PIONEER, a panoramic active mapping framework for efficient large-scale aerial exploration. PIONEER integrates panoramic evidence with a pretrained occupancy prior to estimate cumulative coverage gain over future observation positions. A learned scheduler adapts beam-search width and depth to the mapping state, using predicted runtime and a prescribed planning budget to select search configurations. We also construct a metric-calibrated dataset from 3D assets derived from real-world scenes, providing UE5-compatible textured environments and paired perspective and $360^{\circ }\times 180^{\circ }$ panoramic observations for closed-loop evaluation. Experiments demonstrate that PIONEER improves coverage and reconstruction quality under matched exploration time budgets. Dynamic search allocation further reduces the time to reach a common coverage target relative to the evaluated fixed configuration.