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Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground Penetrating Synthetic Aperture Radar

Authors :
Bähnemann, Rik
Lawrance, Nicholas
Streichenberg, Lucas
Chung, Jen Jen
Pantic, Michael
Grathwohl, Alexander
Waldschmidt, Christian
Siegwart, Roland
Source :
Field Robotics, 2, (2022), 1028-1067
Publication Year :
2021

Abstract

Ground penetrating radar mounted on micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular (GPSAR) trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and inertial measurement unit (IMU) to obtain precise, high rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 us and as precise as 0.1 us with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40% and batch localization accuracy up to 59% compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.<br />Comment: Submitted to Field Robotics journal in June 2021. First revision submitted December 2021

Details

Database :
arXiv
Journal :
Field Robotics, 2, (2022), 1028-1067
Publication Type :
Report
Accession number :
edsarx.2106.10108
Document Type :
Working Paper
Full Text :
https://doi.org/10.55417/fr.2022034