Creating and maintaining an accurate representation of the environment is an essential capability for every service robot. Especially for household robots acting in indoor environments, semantic information is important. In this paper, we present a semantic mapping framework with modular map representations. Our system is capable of online mapping and object updating given object detections from RGB-D data and provides various 2D and 3D~representations of the mapped objects. To undo wrong data associations, we perform a refinement step when updating object shapes. Furthermore, we maintain an existence likelihood for each object to deal with false positive and false negative detections and keep the map updated. Our mapping system is highly efficient and achieves a run time of more than 10 Hz. We evaluated our approach in various environments using two different robots, i.e., a Toyota HSR and a Fraunhofer Care-O-Bot-4. As the experimental results demonstrate, our system is able to generate maps that are close to the ground truth and outperforms an existing approach in terms of intersection over union, different distance metrics, and the number of correct object mappings

翻译：创建和保持环境的准确描述是每个服务机器人的基本能力。 特别是对于室内环境中的家用机器人而言, 语义信息非常重要。 在本文中, 我们提出了一个带有模块化地图显示的语义制图框架。 我们的系统能够从 RGB- D 数据中在线测绘和天体更新给定的天体探测, 并提供所绘制的天体2D 和 3D~ 代表。 为了消除错误的数据关联, 我们在更新天体形状时采取一个改进步骤。 此外, 我们保持每个天体存在的可能性, 以便处理虚假的正反向探测并不断更新地图。 我们的绘图系统效率很高, 运行时间超过 10 赫兹 。 我们使用两种不同的机器人, 即丰田 HSR 和 Fraunhofer Care- O- Bot-4, 评估了我们在不同环境中的方法。 实验结果显示, 我们的系统能够生成接近地面真相的地图, 并且超越现有方法, 超越了连接联盟、 不同距离测量仪和正确天体绘图的数量。