用UWB雷达生成高分辨率图像，让机器人在恶劣环境仍能精准建图。
Novel UWB Synthetic Aperture Radar Imaging for Mobile Robot Mapping
- 通过移动雷达合成大孔径，提升成像清晰度。
- 在模拟恶劣环境下实现高精度环境建图与回环检测。
- 适合需要强鲁棒性的移动机器人感知系统。
移动机器人常用的外部传感器如激光雷达和相机，在能见度低的环境下表现不佳。近年来，超宽带（UWB）雷达因其穿透烟雾、尘埃和雨天等恶劣条件的能力，成为潜在替代方案。然而，由于天线孔径小、方向性弱，单次扫描无法重建视场内详细图像。为此，本文提出一种基于移动机器人路径的UWB雷达合成孔径雷达（SAR）成像流程，通过沿路径移动雷达构建虚拟大孔径，获得高分辨率环境图像。实验评估了SIFT、SURF、BRISK、AKAZE和ORB五种经典特征检测器在UWB SAR图像上进行回环检测的效果，测试在模拟恶劣环境条件下进行。结果表明，UWB SAR成像在高分辨率环境建图与回环检测方面具备可行性与有效性，有助于提升机器人感知系统的鲁棒性与可靠性。
原文摘要 · Abstract (English)
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.
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