Pilot Study of Low-Light Enhanced Terrain Mapping for Robotic Exploration in Lunar PSRs

Remote. Sens. Pub Date : 2023-07-05 DOI:10.3390/rs15133412
Jae-Min Park, Sungchul Hong, H. Shin
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引用次数: 1

Abstract

The recent discovery of water ice in the lunar polar shadowed regions (PSRs) has driven interest in robotic exploration, due to its potential utilization to generate water, oxygen, and hydrogen that would enable sustainable human exploration in the future. However, the absence of direct sunlight in the PSRs poses a significant challenge for the robotic operation to obtain clear images, consequently impacting crucial tasks such as obstacle avoidance, pathfinding, and scientific investigation. In this regard, this study proposes a visual simultaneous localization and mapping (SLAM)-based robotic mapping approach that combines dense mapping and low-light image enhancement (LLIE) methods. The proposed approach was experimentally examined and validated in an environment that simulated the lighting conditions of the PSRs. The mapping results show that the LLIE method leverages scattered low light to enhance the quality and clarity of terrain images, resulting in an overall improvement of the rover’s perception and mapping capabilities in low-light environments.
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面向月球PSRs机器人探测的弱光增强地形测绘试点研究
最近在月球极影区(PSRs)发现的水冰引起了人们对机器人探索的兴趣,因为它可能用于产生水、氧和氢,这将使人类在未来的可持续探索成为可能。然而,psr中缺乏直射阳光对机器人获得清晰图像的操作构成了重大挑战,从而影响了诸如避障、寻路和科学调查等关键任务。为此,本研究提出了一种基于视觉同步定位和测绘(SLAM)的机器人测绘方法,该方法结合了密集测绘和低光图像增强(LLIE)方法。所提出的方法在模拟psr照明条件的环境中进行了实验检验和验证。制图结果表明,LLIE方法利用散射低光增强地形图像的质量和清晰度,整体提升了月球车在低光环境下的感知和制图能力。
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