基于超宽带毫米波FMCW雷达的房间探测同步定位与制图(SLAM)

IF 6.9 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Tobias Körner;Aman Batra;Thomas Kaiser;Nils Pohl;Christian Schulz;Ilona Rolfes;Jan Barowski
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引用次数: 0

摘要

室内场景的同步定位与制图(SLAM)通常以摄像头或激光雷达传感器为数据源。然而,基于雷达的房间扫描为这些系统提供了几个互补的优势。除其他功能外,雷达传感器对光学不透明(例如,在紧急情况下或恶劣环境中由烟雾和灰尘引起的光学不透明)更加强大。此外,雷达传感器的相干测量原理提供了高度精确的距离信息,可用于跟踪可见物体的准确位置和尺寸。与相机和激光雷达相比,雷达在室内探测中的应用目前受到空间分辨率降低的限制,即主要是角/侧向分辨率。这是由于所利用的信号的波长比较大。这项工作展示了工作在80 GHz左右的超宽带毫米波调频连续波(FMCW)雷达传感器与合成孔径雷达(SAR)成像方法的能力。为了允许在移动时成像,评估了基于子孔径处理的自定位技术。因此,我们通过在移动机器人平台上利用超过20 GHz的大绝对带宽和高分辨率成像技术,展示了用于房间探测应用的详细测绘程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simultaneous Localization and Mapping (SLAM) for Room Exploration Using Ultrawideband Millimeterwave FMCW Radar
Simultaneous Localization and Mapping (SLAM) of indoor scenarios is usually based on camera or lidar sensors as data sources. However, radar based room scanning offers several complementary advantages to these systems. Among other features, radar sensors are more robust to optical opacities, for example, those caused by smoke and dust in emergency scenarios or in harsh environments. Furthermore, the coherent measurement principle of radar sensors provides highly precise distance information that can be utilized to track the exact position and dimensions of the visible objects. In contrast to camera and lidar, radar applications in room exploration are up to now limited by reduced spatial, i.e. mostly angular/lateral, resolution. This is due to the comparably large wavelength of the utilized signals. This work demonstrates the capabilities of ultrawideband millimeterwave Frequency Modulated Continuous Wave (FMCW) radar sensors operating around 80 GHz in conjunction with the Synthetic Aperture Radar (SAR) imaging method. To allow imaging whilst moving, self-localization techniques based on sub-aperture processing are evaluated. Therefore, we demonstrate a detailed mapping procedure for room exploration applications by exploiting large absolute bandwidths of more than 20 GHz with high resolution imaging techniques on a mobile robot platform.
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来源期刊
CiteScore
10.70
自引率
0.00%
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审稿时长
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