CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917211158872064 |
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| author | Meshram, Pranay Adhivarahan, Charuvahan Esfahani, Ehsan Tarkesh Chowdhury, Souma Wang, Chen Dantu, Karthik |
| author_facet | Meshram, Pranay Adhivarahan, Charuvahan Esfahani, Ehsan Tarkesh Chowdhury, Souma Wang, Chen Dantu, Karthik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13361 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments Meshram, Pranay Adhivarahan, Charuvahan Esfahani, Ehsan Tarkesh Chowdhury, Souma Wang, Chen Dantu, Karthik Robotics 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. |
| title | CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.13361 |