论文标题
在结构化室内环境中机器人导航的情境图
Situational Graphs for Robot Navigation in Structured Indoor Environments
论文作者
论文摘要
移动机器人应该意识到他们的处境，包括对周围环境的深刻理解，以及对自己的状态的估计，成功地做出明智的决策并在真实环境中自动执行任务。 3D场景图是一个新兴的研究领域，建议在包含几何，语义和关系/拓扑维度的联合模型中代表环境。尽管已经将3D场景图与SLAM技术相结合，以提供机器人的情境理解，但仍需要进一步的研究才能有效部署它们在板载移动机器人。 为此，我们在本文中介绍了一个小说，实时的在线构建情境图（S-Graph），该图在单个优化图中结合在一起，将环境的表示与上述三个维度以及机器人姿势一起。我们的方法利用从3D激光扫描中提取的探音读数和平面表面，实时构造和优化一个三层s-graph，包括（1）机器人跟踪层，该机器人跟踪层注册了机器人的姿势，（2）具有平面墙和（3）层面层的设备的指标式层，该设备具有更高的平面层和更高的室内特征。我们的建议不仅证明了机器人姿势估计的最新结果，而且还以度量的环境模型做出了贡献
Mobile robots should be aware of their situation, comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments. 3D scene graphs are an emerging field of research that propose to represent the environment in a joint model comprising geometric, semantic and relational/topological dimensions. Although 3D scene graphs have already been combined with SLAM techniques to provide robots with situational understanding, further research is still required to effectively deploy them on-board mobile robots. To this end, we present in this paper a novel, real-time, online built Situational Graph (S-Graph), which combines in a single optimizable graph, the representation of the environment with the aforementioned three dimensions, together with the robot pose. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered S-Graph that includes (1) a robot tracking layer where the robot poses are registered, (2) a metric-semantic layer with features such as planar walls and (3) our novel topological layer constraining the planar walls using higher-level features such as corridors and rooms. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantic-topological model of the environment
