CLEAR: 大規模非構造環境のための意味幾何学的地形抽象化
CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments
大規模非構造地形を凸で意味的に整合した領域に抽象化し、地形対応グラフとして表現するフレームワークCLEARを提案。生グリッドA*より最大19.6倍高速な計画と短い実行経路を実現。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Pranay Meshram, Charuvahan Adhivarahan, Ehsan Tarkesh Esfahani, Souma Chowdhury, Chen Wang, Karthik Dantu
分類: cs.RO
原文アブストラクト
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.
