Map construction is the initial step of mobile robots for their localization, navigation, and path planning in unknown environments. Considering the human-robot collaboration (HRC) scenarios in modern manufacturing, where the human workers’ capabilities are closely integrated with the efficiency and precision of robots in the same workspace, a map integrating geometric and semantic information is considered as the technical foundation for intelligent interactions between human workers and robots, such as motion planning, reasoning, and context-aware decision-making. Although different map construction methods have been proposed for mobile robots’ perception in the working environment, it is still a challenging task when applied to such human-robot collaborative manufacturing scenarios to achieve the afore-mentioned intelligent interactions between human workers and robots due to the poor integration of semantic information in the constructed map. On the one hand, due to the lack of ability for differentiating the dynamic objects, the mobile robot might sometimes wrongly use the dynamic objects as the spatial references to calculate the pose transformation between the two successive frames, which negatively affects the accuracy of the robot's localization and pose estimation. On the other hand, the map that integrates both the geometric and semantic information can hardly be constructed in real-time, which cannot provide an effective support for the real-time reasoning and decision making during the human-robot collaboration process.

中文翻译：

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人机协同制造语义图构建方法


地图构建是移动机器人在未知环境中进行定位、导航和路径规划的第一步。考虑到现代制造中的人机协作（HRC）场景，在同一工作空间中，人类工人的能力与机器人的效率和精度紧密结合，集成几何和语义信息的地图被认为是智能的技术基础。人类工人和机器人之间的交互，例如运动规划、推理和上下文感知决策。尽管针对移动机器人在工作环境中的感知提出了不同的地图构建方法，但由于其在人机协同制造场景中实现上述人类工人与机器人之间的智能交互，仍然是一项具有挑战性的任务。构建的地图中语义信息的整合不佳。一方面，由于缺乏区分动态物体的能力，移动机器人有时可能会错误地使用动态物体作为空间参考来计算连续两帧之间的位姿变换，这对机器人定位的准确性产生负面影响。定位和姿态估计。另一方面，融合几何信息和语义信息的地图很难实时构建，无法为人机协作过程中的实时推理和决策提供有效支持。