用语义地图提升机器人规划能力，让机器更懂环境意义。
IntelliMove: Enhancing Robotic Planning with Semantic Mapping
- 构建分层语义拓扑地图框架IntelliMap，融合几何与语义信息
- 基于语义地图的规划显著提升导航效率与任务适应性
- 适用于需理解环境功能的复杂机器人场景
语义导航使机器人不仅能感知环境几何结构，还能理解物体属性、功能及其相互关系。在语义机器人导航中，构建准确且富含语义信息的地图至关重要。基于语义地图的规划不仅提高机器人规划效率与计算速度，还使规划更具意义，支持更广泛的语义任务。本文提出IntelliMove的两大核心模块：IntelliMap——通过分析现有技术优劣构建的通用分层语义拓扑地图框架；以及语义规划模块，利用IntelliMap生成的语义地图进行决策。我们展示了多个应用场景，凸显IntelliMove的适应性与有效性。在模拟环境中实验进一步验证了其在语义导航中的能力。
原文摘要 · Abstract (English)
Semantic navigation enables robots to understand their environments beyond basic geometry, allowing them to reason about objects, their functions, and their interrelationships. In semantic robotic navigation, creating accurate and semantically enriched maps is fundamental. Planning based on semantic maps not only enhances the robot's planning efficiency and computational speed but also makes the planning more meaningful, supporting a broader range of semantic tasks. In this paper, we introduce two core modules of IntelliMove: IntelliMap, a generic hierarchical semantic topometric map framework developed through an analysis of current technologies strengths and weaknesses, and Semantic Planning, which utilizes the semantic maps from IntelliMap. We showcase use cases that highlight IntelliMove's adaptability and effectiveness. Through experiments in simulated environments, we further demonstrate IntelliMove's capability in semantic navigation.
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