Diffraction Separation With Plane-Wave Destruction Filer for the Lunar Penetrating Radar Sensor Data

Song, Haipeng; Sun, Hui; Yang, Jianwen; Mao, He; Liu, Mingchen; Meng, Lu; Chen, Xue

doi:10.1109/jsen.2021.3105324

Cited by 8 publications

(1 citation statement)

References 62 publications

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“…Ground penetrating radar (GPR) is widely utilized in a variety of subsurface inspection applications, including assessment of the lunar surface [1], underground utilities [2], roads [3], pavements [4], detection of land mines [5], as well as concrete structures [6]. GPR signals are frequently interpreted as Bscan images (also known as brightness scan images).…”

Section: Introductionmentioning

confidence: 99%

Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

Wickramanayake¹,

Thiyagarajan²,

Kodagoda³

2022

Preprint

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<p>Sensors help robots perceive their environment and localize themselves. Determining a robot’s location requires a range of sensing systems. Depending on accuracy criteria and navigation conditions, robot localization sensors can differ. Common sensors for robot localization include encoders, GPS, cameras, LIDARs, and IMUs. Traditional sensors are not capable enough in changing environments and uneven terrain. In this paper, we propose a method based on deep learning to use the subsurface features obtained through a Ground Penetrating Radar (GPR) to estimate the odometry of a robot. This proposed method does not rely on visual features or the distance gathered from wheel encoders. The proposed approach was evaluated on a publicly available dataset, and the evaluation results show that the proposed method can be used for robot localization without the need for odometry from wheel encoders.</p>

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Section: Introductionmentioning

confidence: 99%

Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

Wickramanayake¹,

Thiyagarajan²,

Kodagoda³

2022

Preprint

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Enhanced wave modeling & optimal plane-wave destruction (OPWD) method for diffraction separation and imaging

Bashir,

Waheed,

Ali

et al. 2024

Computers & Geosciences

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Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

2022

View full text Add to dashboard Cite

Sensors help robots perceive their environment and localize themselves. Determining a robot's location requires a range of sensing systems. Depending on accuracy criteria and navigation conditions, robot localization sensors can differ. Common sensors for robot localization include encoders, GPS, cameras, LIDARs, and IMUs. Traditional sensors are not capable enough in changing environments and uneven terrain. In this paper, we propose a method based on deep learning to use the subsurface features obtained through a Ground Penetrating Radar (GPR) to estimate the odometry of a robot. This proposed method does not rely on visual features or the distance gathered from wheel encoders. The proposed approach was evaluated on a publicly available dataset, and the evaluation results show that the proposed method can be used for robot localization without the need for odometry from wheel encoders.

show abstract

Diffraction Separation With Plane-Wave Destruction Filer for the Lunar Penetrating Radar Sensor Data

Cited by 8 publications

References 62 publications

Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

Enhanced wave modeling & optimal plane-wave destruction (OPWD) method for diffraction separation and imaging

Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

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