Long-horizon navigation in unstructured environments demands terrain abstractions that scale to tens of km$^2$ while preserving semantic and geometric structure, a combination existing methods fail to achieve. Grids scale poorly; quadtrees misalign with terrain boundaries; neither encodes landcover semantics essential for traversability-aware planning. This yields infeasible or unreliable paths for autonomous ground vehicles operating over 10+ km$^2$ under real-time constraints. CLEAR (Connected Landcover Elevation Abstract Representation) couples boundary-aware spatial decomposition with recursive plane fitting to produce convex, semantically aligned regions encoded as a terrain-aware graph. Evaluated on maps spanning 9-100~km$^2$ using a physics-based simulator, CLEAR achieves up to 10x faster planning than raw grids with only 6.7% cost overhead and delivers 6-9% shorter, more reliable paths than other abstraction baselines. These results highlight CLEAR's scalability and utility for long-range navigation in applications such as disaster response, defense, and planetary exploration.

翻译：非结构化环境中的长程导航需要能够扩展至数十平方公里同时保持语义与几何结构的地形抽象表示，现有方法均无法实现这一组合要求。网格方法扩展性差；四叉树与地形边界难以对齐；两者均未编码对可通行性感知规划至关重要的土地覆盖语义。这导致在实时约束下运行于10平方公里以上区域的自主地面车辆产生不可行或不可靠的路径。CLEAR（连通土地覆盖高程抽象表示）通过将边界感知的空间分解与递归平面拟合相结合，生成以地形感知图编码的凸型语义对齐区域。在9-100平方公里地图上使用基于物理的仿真器进行评估，CLEAR相比原始网格实现高达10倍的规划加速，仅产生6.7%的成本开销，并比其他抽象基线提供短6-9%且更可靠的路径。这些结果凸显了CLEAR在灾害响应、国防及行星探测等应用中长距离导航的可扩展性与实用性。