RoGS：用于大规模路面映射的自适应网格高斯方法

中文摘要 AI 辅助

路面映射在自动驾驶中至关重要，支持高清地图生成、车道级感知和自动道路标注。近期基于网格的路面重建方法有成果，但存在重建质量有限和优化成本高的问题，尤其在大规模驾驶场景中。为此提出ROADGS-T框架，基于自适应网格高斯表示。通过在网格上放置二维高斯面片建模路面，其存储颜色、语义和几何信息。该表示比传统方法更匹配道路薄表面特性，减少冗余和重叠。还引入自适应网格策略，在复杂区域分配更密集面片，在平坦区域保持紧凑表示。此外，设计轨迹一致性引导的姿态稳健细化策略，从多个相邻姿态估计局部表面先验并根据几何一致性自适应加权姿态引导的高度正则化。

英文摘要

Road surface mapping plays a crucial role in autonomous driving, supporting high-definition map generation, lane-level perception, and automatic road annotation. Recent mesh-based road surface reconstruction methods have shown promising results, but they still suffer from limited reconstruction quality and high optimization cost, especially in large-scale driving scenarios. To address these limitations, we propose ROADGS-T, a robust and efficient large-scale road surface mapping framework based on adaptive meshgrid Gaussian representation. Specifically, we model the road surface by placing 2D Gaussian surfels on a meshgrid, where each surfel explicitly stores color, semantic, and geometric information. Compared with conventional mesh-based representations and 3D Gaussian primitives, the proposed meshgrid Gaussian representation better matches the thin-surface property of roads while significantly reducing redundant primitives and overlap during optimization. To further improve representation efficiency and structural fidelity, we introduce a road-structure-aware adaptive meshgrid strategy, which allocates denser Gaussian surfels to geometrically or semantically complex regions, such as lane markings, road boundaries, and height discontinuities, while maintaining a compact representation in flat road areas. Moreover, instead of relying on a single nearest vehicle pose, we design a trajectory-consistency-guided pose-robust refinement strategy, which estimates local surface priors from multiple neighboring poses and adaptively weights pose-guided height regularization according to their geometric consistency.

发表机构

• School of Automation and Intelligent Sensing, Shanghai Jiao Tong university（上海交通大学自动化与智能感知学院）
• State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis（航空电子集成与航空系统-of-系统综合国家重点实验室）
• Shanghai Key Laboratory of Navigation and Location Based Services（上海基于位置服务重点实验室）

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