Traditional exteroceptive sensors in mobile robots, such as LiDARs and cameras often struggle to perceive the environment in poor visibility conditions. Recently, radar technologies, such as ultra-wideband (UWB) have emerged as potential alternatives due to their ability to see through adverse environmental conditions (e.g. dust, smoke and rain). However, due to the small apertures with low directivity, the UWB radars cannot reconstruct a detailed image of its field of view (FOV) using a single scan. Hence, a virtual large aperture is synthesized by moving the radar along a mobile robot path. The resulting synthetic aperture radar (SAR) image is a high-definition representation of the surrounding environment. Hence, this paper proposes a pipeline for mobile robots to incorporate UWB radar-based SAR imaging to map an unknown environment. Finally, we evaluated the performance of classical feature detectors: SIFT, SURF, BRISK, AKAZE and ORB to identify loop closures using UWB SAR images. The experiments were conducted emulating adverse environmental conditions. The results demonstrate the viability and effectiveness of UWB SAR imaging for high-resolution environmental mapping and loop closure detection toward more robust and reliable robotic perception systems.

翻译：移动机器人中传统的外部感知传感器，如激光雷达和摄像头，在能见度较差的条件下往往难以感知环境。近年来，雷达技术（例如超宽带雷达）因其能够穿透恶劣环境条件（如灰尘、烟雾和雨水）而成为潜在的替代方案。然而，由于超宽带雷达孔径较小、方向性较低，单次扫描无法重建其视场的详细图像。因此，通过沿移动机器人路径移动雷达，合成了一个虚拟的大孔径。由此产生的合成孔径雷达图像是周围环境的高清表示。为此，本文提出了一种流程，使移动机器人能够利用基于超宽带雷达的合成孔径雷达成像来绘制未知环境地图。最后，我们评估了经典特征检测器（SIFT、SURF、BRISK、AKAZE和ORB）在使用超宽带合成孔径雷达图像进行回环检测时的性能。实验在模拟恶劣环境条件下进行。结果表明，超宽带合成孔径雷达成像用于高分辨率环境地图构建和回环检测是可行且有效的，有助于实现更稳健可靠的机器人感知系统。