Semantic maps augment traditional representations of robot workspaces, typically based on their geometry and/or topology, with meta-information about the properties, relations and functionalities of their composing elements. A piece of such information could be: fridges are appliances typically found in kitchens and employed to keep food in good condition. Thereby, semantic maps allow for the execution of high-level robotic tasks in an efficient way, e.g. “Hey robot, Store the leftover salad”. This paper presents ViMantic, a novel semantic mapping architecture for the building and maintenance of such maps, which brings together a number of features as demanded by modern mobile robotic systems, including: (i) a formal model, based on ontologies, which defines the semantics of the problem at hand and establishes mechanisms for its manipulation; (ii) techniques for processing sensory information and automatically populating maps with, for example, objects detected by cutting-edge CNNs; (iii) distributed execution capabilities through a client–server design, making the knowledge in the maps accessible and extendable to other robots/agents; (iv) a user interface that allows for the visualization and interaction with relevant parts of the maps through a virtual environment; (v) public availability, hence being ready to use in robotic platforms. The suitability of ViMantic has been assessed using Robot@Home, a vast repository of data collected by a robot in different houses. The experiments carried out consider different scenarios with one or multiple robots, from where we have extracted satisfactory results regarding automatic population, execution times, and required size in memory of the resultant semantic maps.

语义地图增强了机器人工作空间的传统表示，通常基于它们的几何形状和/或拓扑结构，以及有关其组成元素的属性、关系和功能的元信息。一条这样的信息可能是：冰箱是厨房里常见的电器，用来保持食物的良好状态。因此，语义地图允许以有效的方式执行高级机器人任务，例如 “嘿机器人，存储剩余的沙拉”。本文介绍了ViMantic，一种用于构建和维护此类地图的新型语义映射架构，它汇集了现代移动机器人系统所要求的许多特征，包括：（i）基于本体的形式模型，它定义了问题的语义手头并建立操纵机制；(ii) 处理感官信息和自动填充地图的技术，例如，尖端 CNN 检测到的对象；(iii) 通过客户端 - 服务器设计的分布式执行能力，使地图中的知识可以访问并扩展到其他机器人/代理；(iv) 允许通过虚拟环境与地图的相关部分进行可视化和交互的用户界面；(v) 公开可用，因此可以在机器人平台中使用。的适用性ViMantic已使用 Robot@Home 进行评估，Robot@Home 是一个由机器人在不同房屋中收集的庞大数据存储库。进行的实验考虑了一个或多个机器人的不同场景，我们从中提取了令人满意的结果，这些结果涉及自动填充、执行时间和结果语义图的内存中所需的大小。