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Article

Robotic Mapping Approach under Illumination-Variant Environments at Planetary Construction Sites

1
Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea
2
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, College of Engineering, Daejeon 34141, Korea
3
Department of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Kaichang Di, Long Xiao and Jessica Flahaut
Remote Sens. 2022, 14(4), 1027; https://doi.org/10.3390/rs14041027
Received: 25 January 2022 / Revised: 14 February 2022 / Accepted: 17 February 2022 / Published: 20 February 2022
(This article belongs to the Special Issue Planetary Geologic Mapping and Remote Sensing)
In planetary construction, the semiautonomous teleoperation of robots is expected to perform complex tasks for site preparation and infrastructure emplacement. A highly detailed 3D map is essential for construction planning and management. However, the planetary surface imposes mapping restrictions due to rugged and homogeneous terrains. Additionally, changes in illumination conditions cause the mapping result (or 3D point-cloud map) to have inconsistent color properties that hamper the understanding of the topographic properties of a worksite. Therefore, this paper proposes a robotic construction mapping approach robust to illumination-variant environments. The proposed approach leverages a deep learning-based low-light image enhancement (LLIE) method to improve the mapping capabilities of the visual simultaneous localization and mapping (SLAM)-based robotic mapping method. In the experiment, the robotic mapping system in the emulated planetary worksite collected terrain images during the daytime from noon to late afternoon. Two sets of point-cloud maps, which were created from original and enhanced terrain images, were examined for comparison purposes. The experiment results showed that the LLIE method in the robotic mapping method significantly enhanced the brightness, preserving the inherent colors of the original terrain images. The visibility and the overall accuracy of the point-cloud map were consequently increased. View Full-Text
Keywords: planetary construction; robotic mapping; SLAM; low-light enhancement; 3D point-cloud map; deep learning planetary construction; robotic mapping; SLAM; low-light enhancement; 3D point-cloud map; deep learning
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MDPI and ACS Style

Hong, S.; Shyam, P.; Bangunharcana, A.; Shin, H. Robotic Mapping Approach under Illumination-Variant Environments at Planetary Construction Sites. Remote Sens. 2022, 14, 1027. https://doi.org/10.3390/rs14041027

AMA Style

Hong S, Shyam P, Bangunharcana A, Shin H. Robotic Mapping Approach under Illumination-Variant Environments at Planetary Construction Sites. Remote Sensing. 2022; 14(4):1027. https://doi.org/10.3390/rs14041027

Chicago/Turabian Style

Hong, Sungchul, Pranjay Shyam, Antyanta Bangunharcana, and Hyuseoung Shin. 2022. "Robotic Mapping Approach under Illumination-Variant Environments at Planetary Construction Sites" Remote Sensing 14, no. 4: 1027. https://doi.org/10.3390/rs14041027

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