用先验地图提升复杂建筑导航准确率
PM-Nav: Priori-Map Guided Embodied Navigation in Functional Buildings
- 将环境地图转为语义先验图，引导路径规划
- 仿真与真实场景分别提升511%和1175%成功率
- 适合智能机器人在功能型建筑中自主导航
现有语言驱动的具身导航方法在功能型建筑（FBs）中面临挑战，因建筑特征高度相似，难以有效利用先验空间知识。为此，我们提出先验地图引导的具身导航（PM-Nav），将环境地图转化为导航友好的语义先验图，设计带有标注先验图的分层思维链提示模板以实现精准路径规划，并构建多模型协同动作输出机制，完成定位决策与导航执行控制。在自建的FB数据集上进行的综合测试表明，PM-Nav在仿真和真实场景中分别相对于SG-Nav和InstructNav获得平均511%、1175%和650%、400%的提升。这些显著增益充分展现了PM-Nav作为功能型建筑导航框架的巨大潜力。
原文摘要 · Abstract (English)
Existing language-driven embodied navigation paradigms face challenges in functional buildings (FBs) with highly similar features, as they lack the ability to effectively utilize priori spatial knowledge. To tackle this issue, we propose a Priori-Map Guided Embodied Navigation (PM-Nav), wherein environmental maps are transformed into navigation-friendly semantic priori-maps, a hierarchical chain-of-thought prompt template with an annotation priori-map is designed to enable precise path planning, and a multi-model collaborative action output mechanism is built to accomplish positioning decisions and execution control for navigation planning. Comprehensive tests using a home-made FB dataset show that the PM-Nav obtains average improvements of 511\% and 1175\%, and 650\% and 400\% over the SG-Nav and the InstructNav in simulation and real-world, respectively. These tremendous boosts elucidate the great potential of using the PM-Nav as a backbone navigation framework for FBs.
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