用UWB雷达与测向技术实现无视觉环境下的精准建图
All-UWB SLAM Using UWB Radar and UWB AOA
- 通过动态部署的UWB锚点获取角度信息，增强雷达感知
- 在特征匮乏场景下实现稳定建图，定位误差降低40%以上
- 适合烟雾、灰尘等光学传感器失效的复杂环境使用
针对烟雾、尘埃等导致可见光传感器失效的恶劣环境，本文提出一种融合超宽带（UWB）雷达与角度到达（AOA）测量的新型全UWB SLAM方法。现有方法依赖环境中可辨识特征，而在特征稀少区域性能受限。为此，本研究利用机器人在建图过程中动态部署的UWB锚点-标签单元，实时获取AOA数据，弥补雷达在特征缺失环境中的感知不足。论文系统分析了UWB AOA测量中的典型限制，并提出有效解决方案。实验结果表明，结合UWB AOA的雷达系统可在无视觉、低特征环境中实现稳定且高精度的定位与建图。
原文摘要 · Abstract (English)
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.
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