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TerrFlat: Physics-Driven Geometry Representation for Structure-Aware Freespace Detection
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DOI：10.1109/LRA.2026.3655205.png)
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摘要
En 中文
Freespace detection in autonomous driving is limited by the lack of explicit geometric modeling, hindering generalization across complex terrains. Existing approaches are predominantly data-driven and neglect the physical structure of drivable surfaces. We propose Terrain Flat (TerrFlat), a physics-driven geometric representation that models road surfaces along three interpretable dimensions: lateral smoothness, longitudinal consistency, and vertical deviation. TerrFlat is constructed through geometric reasoning and projected into pixel-aligned maps via a differentiable projection, ensuring geometric–visual consistency. Building on this representation, we introduce a symmetric feature fusion module (SFFM) to integrate TerrFlat with visual features through bidirectional recalibration, improving semantic discrimination and boundary localization. Together, TerrFlat and SFFM form TerrFlat-Seg, a unified framework for physics-aware freespace perception. Experiments on KITTI-Road, Semantic-KITTI, and the off-road ORFD datasets demonstrate consistent improvements over existing baselines. Real-world validation on an automated guided vehicle platform further confirms the robustness of our approach.
Keyword:
Freespace detection
physics-driven geometric representation
geometric-visual fusion
autonomous vehicles
mobile robotics
期刊
I
IF:
5.3
论文数:
1.9K
被引数:
3.9W
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