CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments

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 10+ km2. CLEAR (Connected LandcoverElevation Abstract Representation) is a reusable terrain abstraction framework for large-scale planning producing convex, semantically aligned regions encoded as a terrain-aware graph. Evaluated on 9-100 km2 digital terrain maps with physics-based simulation, CLEAR reduces per-query planning time by 2× to 19.6× over unabstracted raw-grid A*after one-time abstraction with 6.7% cost overhead. In physics-based simulation, CLEAR delivers 5-8.6% shorter executed paths and 100% task completion across all maps versus 90-100% for Quadtree. These results hold against AMRA*, 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.