Autonomous exploration with UAVs in large-scale, topologically complex environments often suffers from low efficiency due to suboptimal scheduling and detours. Prior maps (e.g., construction drawings), although usually imprecise and flawed, are readily available in many scenarios and have the potential to provide global structural guidance. This paper presents a novel exploration framework that leverages sparse, unaligned, and even discrepant 2D prior maps for LiDAR-based UAV exploration. First, a robust 2D-3D point cloud registration pipeline is proposed to align LiDAR observations with prior maps. The registration pipeline combines a GeoContext descriptor for single-frame candidate retrieval, a multi-frame verification mechanism for coarse transformation estimation with outlier rejection, and a Scale-ICP algorithm for refinement. The registration module can handle map discrepancies and provide multiple hypotheses when geometric ambiguities arise. To effectively utilize the registration results for exploration planning, we further develop a hierarchical viewpoint planning strategy under localization uncertainties. The hierarchical strategy first spatially attaches local viewpoints to prior guidepoints and adopts a Monte Carlo Tree Search solver to determine their traversal sequence under each registration hypothesis. To mitigate registration uncertainty, a risk-aware selector evaluates prior sequences using confidence-weighted travel risk, and a fixed-endpoint traveling salesman problem is formulated to generate an efficient local coverage path under the selected prior guidance. Benchmark evaluations reveal up to 34.2% improvement in exploration efficiency and 37.9% reduction in flight distance compared to state-of-the-art methods, while extensive simulations and field experiments further demonstrate robustness to prior map incompleteness and deformations.

翻译：在大型、拓扑复杂的环境中进行无人机自主探索时，由于次优调度和迂回路径，往往面临效率低下的问题。先验地图（如施工图纸）虽然通常不精确且存在缺陷，但在许多场景中易于获取，并具有提供全局结构指导的潜力。本文提出了一种新颖的探索框架，利用稀疏、未对齐甚至存在偏差的二维先验地图，实现基于激光雷达的无人机探索。首先，提出了一种鲁棒的2D-3D点云配准流程，将激光雷达观测与先验地图对齐。该配准流程结合了用于单帧候选检索的GeoContext描述符、用于粗变换估计与离群点剔除的多帧验证机制，以及用于精化的Scale-ICP算法。该配准模块能够处理地图偏差，并在几何歧义出现时提供多种假设。为了有效利用配准结果进行探索规划，我们进一步开发了一种考虑定位不确定性的分层视角规划策略。该分层策略首先将局部视角空间附着于先验引导点，并采用蒙特卡洛树搜索求解器确定其在各配准假设下的遍历顺序。为缓解配准不确定性，一种风险感知选择器利用置信度加权的行程风险评估先验序列，并构建了固定端点的旅行商问题，在选定先验指导下生成高效的局部覆盖路径。基准评估显示，与最先进方法相比，探索效率提升高达34.2%，飞行距离减少37.9%；广泛的仿真与实地实验进一步证明了该方法对先验地图不完整与形变的鲁棒性。