用增量图结构实现多层建筑高效自主探索
Multi-Floor Exploration for Ground Robots via an Incremental Reachable Graph and Structural Priors
- 构建增量可扩展的可达图，保留潜在连通性
- 通过投影先验提升跨楼层探索效率，减少冗余搜索
- 适合需在复杂建筑中导航的地面机器人使用
地面机器人在多层建筑中的自主探索仍具挑战性，因传统2D和2.5D地图无法表达楼梯、坡道等重叠可通行表面及多层可达性。本文提出基于增量可达图的多层探索框架：该图以可通行支撑面为节点构建，即使在稀疏观测下仍保留潜在连通性，支持稳定且物理可行的前沿检测。为引导当前楼层外的探索，将已探索楼层的任务区域先验投影至目标楼层，初始化假设图，并随新观测逐步修正。分层规划器联合推理确认与假设结构，实现全局引导。仿真结果表明，该方法相较基线显著提升探索效率与建图完整性；实机实验验证了其实际可行性与实时性能。
原文摘要 · Abstract (English)
Autonomous exploration of multi-floor buildings remains challenging for ground robots because conventional 2D and 2.5D maps cannot represent overlapping traversable surfaces such as stairs, ramps, and multiple reachable elevations. This letter presents a multi-floor exploration framework based on an incremental reachable graph. Built as a sparse graph over reachable support surfaces, the graph preserves potentially valid connectivity through tentative graph elements under sparse observations and enables stable, physically reachable frontier detection. To guide exploration beyond the currently mapped floor, we project task-zone priors from an explored floor to initialize a hypothetical graph on the target floor and reconcile it incrementally with incoming observations. A hierarchical planner then jointly reasons over confirmed and hypothetical structures for global guidance. In simulation, the proposed method demonstrates improved exploration efficiency and mapping completeness compared to evaluated baselines. Furthermore, onboard real-world experiments validate its practical feasibility and real-time performance.
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