Reciprocal Visibility for Guided Occlusion Removal With Drones

Rakesh John Amala Arokia Nathan; Sigrid Strand; Dmitriy Shutin; Oliver Bimber

doi:10.1109/LGRS.2024.3451486

Reciprocal Visibility for Guided Occlusion Removal With Drones

Rakesh John Amala Arokia Nathan, Sigrid Strand, Dmitriy Shutin, Oliver Bimber

Institute of Computer Graphics

Research output: Contribution to journal › Article › peer-review

Abstract

In this letter, a guidance strategy is proposed to optimize synthetic aperture sampling for occlusion removal with drones based on point-cloud representation of occluders. Prerecorded light detecting and ranging (LiDAR) scans are utilized to compute the visibility of fixed inspection regions on the ground that are intended for recurrent monitoring from the air. By utilizing Helmholtz reciprocity, the drone-collected LiDAR scans are used to computationally obtain a reciprocal visibility (RV) of potential drone positions in the air from points of interest on the ground. This visibility forms a basis for a novel navigation strategy. This strategy was shown to drive drones to optimal aerial monitoring positions, thus reducing occlusion and, consequently, the sampling time. Compared with previous unguided sampling, we achieve a 5%–20% higher visibility with 9–17 times less samples in our experiments.

Original language	English
Article number	6502205
Pages (from-to)	1-5
Number of pages	5
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	21
DOIs	https://doi.org/10.1109/LGRS.2024.3451486
Publication status	Published - Aug 2024

Fields of science

102 Computer Sciences
102003 Image processing
102008 Computer graphics
102015 Information systems
102020 Medical informatics
103021 Optics

JKU Focus areas

Digital Transformation

Access to Document

10.1109/LGRS.2024.3451486

Cite this

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title = "Reciprocal Visibility for Guided Occlusion Removal With Drones",

abstract = "In this letter, a guidance strategy is proposed to optimize synthetic aperture sampling for occlusion removal with drones based on point-cloud representation of occluders. Prerecorded light detecting and ranging (LiDAR) scans are utilized to compute the visibility of fixed inspection regions on the ground that are intended for recurrent monitoring from the air. By utilizing Helmholtz reciprocity, the drone-collected LiDAR scans are used to computationally obtain a reciprocal visibility (RV) of potential drone positions in the air from points of interest on the ground. This visibility forms a basis for a novel navigation strategy. This strategy was shown to drive drones to optimal aerial monitoring positions, thus reducing occlusion and, consequently, the sampling time. Compared with previous unguided sampling, we achieve a 5%–20% higher visibility with 9–17 times less samples in our experiments.",

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AU - Shutin, Dmitriy

AU - Bimber, Oliver

PY - 2024/8

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AB - In this letter, a guidance strategy is proposed to optimize synthetic aperture sampling for occlusion removal with drones based on point-cloud representation of occluders. Prerecorded light detecting and ranging (LiDAR) scans are utilized to compute the visibility of fixed inspection regions on the ground that are intended for recurrent monitoring from the air. By utilizing Helmholtz reciprocity, the drone-collected LiDAR scans are used to computationally obtain a reciprocal visibility (RV) of potential drone positions in the air from points of interest on the ground. This visibility forms a basis for a novel navigation strategy. This strategy was shown to drive drones to optimal aerial monitoring positions, thus reducing occlusion and, consequently, the sampling time. Compared with previous unguided sampling, we achieve a 5%–20% higher visibility with 9–17 times less samples in our experiments.

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