通过拓扑约束减少复杂3D环境中的导航状态空间，提升效率与成功率。
Beyond Geometry: Efficient Topologically-Grounded Navigation in Complex 3D Environments
- 基于地面支撑、上方净空和种子连通性构建可行走位置的简化状态空间
- 在Matterport3D场景中状态空间压缩超80%，A*搜索耗时低于1毫秒
- 适用于室内机器人导航，尤其适合高精度实时路径规划需求
复杂3D环境中地面机器人的导航常受几何模糊性制约，非通行结构（如家具）与可通行地面具有相似局部几何特征。同时，大规模体素空间搜索的计算开销巨大。为此，本文提出一种表面提取框架，通过强制地面支撑、上方净空及种子连通性约束，构建物理可达站立位置的精简状态空间。在五个Matterport3D室内场景和三个PCT基准场景上评估，状态空间压缩超过80%，Matterport3D场景下A*搜索耗时低于1毫秒，所有300个测试查询均实现100%规划成功。
原文摘要 · Abstract (English)
Ground robot navigation in complex 3D environments is often hindered by geometric ambiguity, where non-traversable structures such as furniture share local geometric properties with navigable ground. Furthermore, the computational cost of searching massive voxel spaces remains a significant challenge. To address these issues, we present a surface extraction framework that constructs a reduced state space of physically reachable standing positions by enforcing ground support, overhead clearance, and seed-based connectivity constraints. Evaluation across five Matterport3D indoor scenes and three PCT benchmark scenes demonstrates over 80\% state space reduction and sub-millisecond A* search on the Matterport3D scenes, with 100\% planning success across all 300 tested queries.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。