在线面向对象的语义建图与地图更新
Online Object-Oriented Semantic Mapping and Map Updating
AI总结：
本文提出模块化在线面向对象语义建图框架，利用RGB-D检测更新物体、修正错误关联并维护存在似然，可在两台机器人上以超过10 Hz生成更接近真值的地图。
摘要：
创建并维护准确的环境表示是每台服务机器人的基本能力。尤其是对于在室内环境中工作的家用机器人而言，语义信息十分重要。本文提出了一种具有模块化地图表示的语义建图框架。我们的系统能够基于RGB-D数据中的物体检测结果进行在线建图和物体更新，并提供已建图物体的多种2D和3D表示。为撤销错误的数据关联，我们在更新物体形状时执行一个细化步骤。此外，我们为每个物体维护一个存在似然，以处理误报和漏报检测，并保持地图处于最新状态。我们的建图系统非常高效，运行频率超过10 Hz。我们使用两台不同的机器人，即Toyota HSR和Fraunhofer Care-O-Bot-4，在多种环境中评估了所提方法。实验结果表明，我们的系统能够生成接近真值的地图，并且在交并比、不同距离度量以及正确物体建图数量方面优于一种现有方法。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
