2020
DOI: 10.1109/lra.2019.2959490
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Improving Visual Feature Extraction in Glacial Environments

Abstract: Glacial science could benefit tremendously from autonomous robots, but previous glacial robots have had perception issues in these colorless and featureless environments, specifically with visual feature extraction. Glaciologists use near-infrared imagery to reveal the underlying heterogeneous spatial structure of snow and ice, and we theorize that this hidden near-infrared structure could produce more and higher quality features than available in visible light. We took a custom camera rig to Igloo Cave at Mt.… Show more

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Cited by 4 publications
(2 citation statements)
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“…Kong et al [18] proposed a direct near-infrared depth visual SLAM method, employing a direct bundle adjustment approach to jointly fuse near-infrared and depth images for optimizing camera motion estimation. Moreover, Morad et al [19] conducted research specifically on feature point extraction from near-infrared images and highlighted the need for further investigation into visual odometry using near-infrared imagery.…”
Section: Introductionmentioning
confidence: 99%
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“…Kong et al [18] proposed a direct near-infrared depth visual SLAM method, employing a direct bundle adjustment approach to jointly fuse near-infrared and depth images for optimizing camera motion estimation. Moreover, Morad et al [19] conducted research specifically on feature point extraction from near-infrared images and highlighted the need for further investigation into visual odometry using near-infrared imagery.…”
Section: Introductionmentioning
confidence: 99%
“…This characteristic offers significant advantages. Additionally, studies by Morad et al [19] suggest that in glacier environments, near-infrared images can serve as substitutes for or even outperform visible light images. Consequently, our study will also conduct experiments to validate the relative quality of near-infrared and visible light imaging under challenging lighting conditions.…”
Section: Introductionmentioning
confidence: 99%