Autonomous Robotic Exploration and Mapping of Smart Indoor Environments With UWB-IoT Devices
Autonomous Robotic Exploration and Mapping of Smart Indoor Environments With UWB-IoT Devices
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发表时间：
2019
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通讯作者：
Tianyi Wang;Ke Huo;Muzhi Han;Daniel R. McArthur;Ze An;David Cappeleri;K. Ramani
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作者：
Tianyi Wang;Ke Huo;Muzhi Han;Daniel R. McArthur;Ze An;David Cappeleri;K. Ramani
新兴的同步定位和映射（SLAM）技术使机器人具有物理世界的空间意识。然而，这种意识仍然停留在几何水平上。提出了一种基于语义标签快速构建智能环境的方法，以增强机器人的空间智能。从本质上讲，我们将基于uwb的距离传感物联网设备嵌入到常规物品中，并将机器人视为物联网网络中的动态节点。通过利用机器人节点的自定位，我们在SLAM地图中解析物联网设备的位置。然后，我们利用物联网的语义知识，使机器人能够在智能环境中导航和交互。随着物联网节点的部署，机器人可以适应未知环境或具有时变配置的环境。通过我们的实验，我们证明了我们的方法支持对象级别的定位精度(~ 0。28 m)，较短的发现和定位时间(118。6 s)与穷举搜索相比，以及针对全局方法和局部操作的有效导航策略。此外，我们还演示了两个用例场景，其中服务机器人(i)在智能家居中执行一系列用户分配的任务，（ii）使用物联网地标探索多个连接区域。
The emerging simultaneous localization and mapping (SLAM) techniques enable robots with spatial awareness of the physical world. However, such awareness remains at a geometric level. We propose an approach for quickly constructing a smart environment with semantic labels to enhance the robot with spatial intelligence. Essentially, we embed UWB-based distance sensing IoT devices into regular items and treat the robot as a dynamic node in the IoT network. By leveraging the self-localization from the robot node, we resolve the locations of IoT devices in the SLAM map. We then exploit the semantic knowledge from the IoT to enable the robot to navigate and interact within the smart environment. With the IoT nodes deployed, the robot can adapt to environments that are unknown or that have time-varying configurations. Through our experiments, we demonstrated that our method supports an object level of localization accuracy ( ∼ 0 . 28 m ), a shorter discovery and localization time ( 118 . 6 s ) compared to an exhaustive search, and an effective navigation strategy for a global approach and local manipulation. Further, we demonstrated two use case scenarios where a service robot (i) executes a sequence of user-assigned tasks in a smart home and (ii) explores multiple connected regions using IoT landmarks.