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

TL;DR

CLEAR introduces a scalable terrain abstraction that combines semantic and geometric information, enabling efficient and reliable long-range navigation for autonomous vehicles in large unstructured environments.

Contribution

The paper presents CLEAR, a novel terrain abstraction method that couples boundary-aware spatial decomposition with recursive plane fitting for large-scale, semantic-aware terrain modeling.

Findings

CLEAR achieves up to 10x faster planning than raw grid methods.

It produces 6-9% shorter, more reliable paths compared to other baselines.

Evaluations on maps spanning 9-100 km² demonstrate scalability and effectiveness.

Abstract

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.