LiDAR and cameras are frequently used as sensors for simultaneous localization and mapping (SLAM). However, these sensors are prone to failure under low visibility (e.g. smoke) or places with reflective surfaces (e.g. mirrors). On the other hand, electromagnetic waves exhibit better penetration properties when the wavelength increases, thus are not affected by low visibility. Hence, this paper presents ultra-wideband (UWB) radar as an alternative to the existing sensors. UWB is generally known to be used in anchor-tag SLAM systems. One or more anchors are installed in the environment and the tags are attached to the robots. Although this method performs well under low visibility, modifying the existing infrastructure is not always feasible. UWB has also been used in peer-to-peer ranging collaborative SLAM systems. However, this requires more than a single robot and does not include mapping in the mentioned environment like smoke. Therefore, the presented approach in this paper solely depends on the UWB transceivers mounted on-board. In addition, an extended Kalman filter (EKF) SLAM is used to solve the SLAM problem at the back-end. Experiments were conducted and demonstrated that the proposed UWB-based radar SLAM is able to map natural point landmarks inside an indoor environment while improving robot localization.

翻译：激光雷达与摄像头常被用作同步定位与地图构建的传感器。然而，在低可见度（如烟雾）或存在反射表面（如镜面）的环境中，这些传感器容易失效。另一方面，电磁波随波长增加表现出更强的穿透特性，因而不会受到低可见度的影响。为此，本文提出将超宽带雷达作为现有传感器的替代方案。超宽带技术通常应用于锚点-标签式SLAM系统，即在环境中安装一个或多个锚点，并将标签附着于机器人。尽管该方法在低可见度环境下表现良好，但改造现有基础设施并非总是可行。超宽带也被用于点对点测距协同SLAM系统中，但这需要多台机器人协同工作，且无法在烟雾等特定环境中实现地图构建。因此，本文提出的方法完全依赖于车载超宽带收发器。此外，采用扩展卡尔曼滤波SLAM作为后端求解SLAM问题。实验证明，所提基于超宽带雷达的SLAM能够在室内环境中绘制自然点状地标，同时提升机器人定位精度。