使用UWB雷达与UWB AOA的全UWB SLAM
All-UWB SLAM Using UWB Radar and UWB AOA
AI总结：
本文提出将UWB到达角测量融入UWB雷达SLAM系统，通过机器人动态部署锚点-标签单元，解决视觉受限且特征匮乏环境下的定位与建图精度和可扩展性问题。
摘要：
人们对设计用于在可见光光谱失效的恶劣条件（如烟雾、灰尘）下工作的自主系统越来越感兴趣。在这种背景下，超宽带（UWB）雷达由于其宽带宽内的较低频率分量，能够穿透此类具有挑战性的环境条件。因此，UWB雷达已成为在视觉受限环境中进行同时定位与建图（SLAM）的一种潜在感知技术，在这些环境中光学传感器（如LiDAR、相机）容易失效。现有以UWB雷达作为主要外部感知传感器的方法通常提取环境中的特征，随后将这些特征初始化为地图中的路标。然而，这些方法受限于环境中可区分特征的数量。因此，本文提出了一种新颖的方法，将UWB到达角（AOA）测量融入基于UWB雷达的SLAM系统中，以提高在特征匮乏环境中SLAM的精度和可扩展性。AOA测量通过UWB锚点-标签单元获得，这些单元由机器人在环境建图过程中动态部署在无特征区域。本文深入讨论了与UWB AOA测量单元相关的现有约束，并提出了克服这些约束的解决方案。我们的实验结果表明，将UWB AOA单元与UWB雷达集成，能够在视觉受限且特征匮乏的环境中实现SLAM。
英文摘要：
There has been a growing interest in autonomous systems designed to operate in adverse conditions (e.g. smoke, dust), where the visible light spectrum fails. In this context, Ultra-wideband (UWB) radar is capable of penetrating through such challenging environmental conditions due to the lower frequency components within its broad bandwidth. Therefore, UWB radar has emerged as a potential sensing technology for Simultaneous Localization and Mapping (SLAM) in vision-denied environments where optical sensors (e.g. LiDAR, Camera) are prone to failure. Existing approaches involving UWB radar as the primary exteroceptive sensor generally extract features in the environment, which are later initialized as landmarks in a map. However, these methods are constrained by the number of distinguishable features in the environment. Hence, this paper proposes a novel method incorporating UWB Angle of Arrival (AOA) measurements into UWB radar-based SLAM systems to improve the accuracy and scalability of SLAM in feature-deficient environments. The AOA measurements are obtained using UWB anchor-tag units which are dynamically deployed by the robot in featureless areas during mapping of the environment. This paper thoroughly discusses prevailing constraints associated with UWB AOA measurement units and presents solutions to overcome them. Our experimental results show that integrating UWB AOA units with UWB radar enables SLAM in vision-denied feature-deficient environments.
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。
