Abstract Due to its vast applicability, the semantic interpretation of regions or entities increasingly attracts the attention of scholars within the robotics community. The paper at hand introduces a novel unsupervised technique to semantically identify the position of an autonomous agent in unknown environments. When the robot explores a certain path for the first time, community detection is achieved through graph-based segmentation. This allows the agent to semantically define its surroundings in future traverses even if the environment’s lighting conditions are changed. The proposed semantic clustering technique exploits the Louvain community detection algorithm, which constitutes a novel and efficient method for identifying groups of measurements with consistent similarity. The produced communities are combined with metric information, as provided by the robot’s odometry through a hierarchical agglomerative clustering method. The suggested algorithm is evaluated in indoors and outdoors datasets creating topological maps capable of assisting semantic localization. We demonstrate that the system categorizes the places correctly when the robot revisits an environment despite the possible lighting variation.

中文翻译：

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移动机器人任务的无监督语义聚类和定位

摘要 由于其广泛的适用性，区域或实体的语义解释越来越受到机器人学界学者的关注。手头的论文介绍了一种新颖的无监督技术，可以从语义上识别未知环境中自主代理的位置。当机器人第一次探索某条路径时，通过基于图的分割来实现社区检测。即使环境的光照条件发生变化，这也允许代理在未来的遍历中从语义上定义其周围环境。所提出的语义聚类技术利用了 Louvain 社区检测算法，该算法构成了一种新颖且有效的方法，用于识别具有一致相似性的测量组。产生的社区与度量信息相结合，由机器人的里程计通过分层凝聚聚类方法提供。建议的算法在室内和室外数据集中进行评估，创建能够帮助语义定位的拓扑图。我们证明，尽管可能存在照明变化，但当机器人重新访问环境时，系统会正确地对地点进行分类。