CLEAR让大场景导航更高效，用语义几何融合方法抽象地形图
CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments
- 构建语义对齐的凸区域图，实现千米级地形抽象
- 查询规划速度提升2到19.6倍，路径比网格法短5%~8.6%
- 适用于大规模自主导航，不依赖具体成本模型
在超过10平方公里的非结构化环境中进行长程导航，需要能扩展至数十平方公里且保留语义与几何结构的地形抽象。传统网格扩展性差，四叉树又与地形边界错位，均无法有效编码影响通行性的语义信息，导致路径不可行或效率低下。本文提出可复用的地形抽象框架CLEAR（Connected Landcover Elevation Abstract Representation），生成语义对齐的凸区域，并以地形感知图形式表示。在覆盖9至100平方公里的数字地形图上，通过物理仿真验证：经一次抽象后，相比原始网格A*，CLEAR使单次查询规划时间减少2至19.6倍，仅增加6.7%开销；在物理仿真中，路径长度缩短5%~8.6%，任务完成率100%，优于四叉树的90%~100%。结果对AMRA-star等强基准也成立，并可推广至学习型高程驱动代价函数，证明CLEAR在可扩展性和成本模型独立性上的优势。
原文摘要 · Abstract (English)
Long-horizon navigation in unstructured environments demands terrain abstractions that scale to tens of square kilometers while preserving semantic and geometric structure. Grids scale poorly, and quadtrees misalign with terrain boundaries. Neither encodes terrain semantics essential for traversability-aware planning, yielding infeasible or inefficient paths for autonomous ground vehicles operating over more than 10 square kilometers. CLEAR (Connected Landcover Elevation Abstract Representation) is a reusable terrain abstraction framework for large-scale planning that produces convex, semantically aligned regions encoded as a terrain-aware graph. Evaluated on digital terrain maps spanning 9 to 100 square kilometers with physics-based simulation, CLEAR reduces per-query planning by 2x to 19.6x over unabstracted raw-grid A-star after one-time abstraction, with 6.7 percent cost overhead. In physics-based simulation, CLEAR delivers 5 to 8.6 percent shorter executed paths and 100 percent task completion across all maps, compared with 90 to 100 percent for Quadtree. These results hold against AMRA-star, a strong anytime multi-resolution planner, and generalize to a learned elevation-driven cost, demonstrating CLEAR's scalability and cost-model independence as a reusable planning layer.
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