水下视觉单目SLAM挑战研究
Investigation of the Challenges of Underwater-Visual-Monocular-SLAM
AI总结：
本文系统评估单目vSLAM在真实AUV与实验室水下场景中的性能退化，分析水体光学和光照影响，并考察标定机动与图像复原对准确性和鲁棒性的提升。
摘要：
本文对用于水下机器人的单目视觉同步定位与地图构建（vSLAM）方法所面临的挑战进行了全面研究。尽管过去十年间利用视觉数据的状态估计方法已取得显著进展，但大多数评估仍局限于受控的室内和城市环境，并在这些环境中展现出令人瞩目的性能。然而，这些技术尚未在极具挑战性的条件下得到广泛测试，例如水下场景；在该场景中，水体与光照条件、机器人路径以及深度等因素都会显著影响算法性能。因此，我们的评估既在真实AUV场景中开展，也在能够提供精确外部参考的实验室环境中进行。研究重点在于理解环境条件（例如水体的光学特性和照明场景）对单目vSLAM方法性能的影响。为此，我们首先表明所有方法在空气中设置下均表现出色，随后展示其在具有挑战性的水下环境中的性能下降。本研究的最终目标是识别能够在此类条件下提升SLAM方法准确性和鲁棒性的技术。为实现这一目标，我们研究了图像增强技术在改善SLAM方法所用输入图像质量方面的潜力，特别是在散射介质中的低能见度和极端光照场景下。我们首次评估了标定机动和简单图像复原技术，以确定它们在水下环境中启用或增强单目SLAM方法性能的能力。
英文摘要：
In this paper, we present a comprehensive investigation of the challenges of Monocular Visual Simultaneous Localization and Mapping (vSLAM) methods for underwater robots. While significant progress has been made in state estimation methods that utilize visual data in the past decade, most evaluations have been limited to controlled indoor and urban environments, where impressive performance was demonstrated. However, these techniques have not been extensively tested in extremely challenging conditions, such as underwater scenarios where factors such as water and light conditions, robot path, and depth can greatly impact algorithm performance. Hence, our evaluation is conducted in real-world AUV scenarios as well as laboratory settings which provide precise external reference. A focus is laid on understanding the impact of environmental conditions, such as optical properties of the water and illumination scenarios, on the performance of monocular vSLAM methods. To this end, we first show that all methods perform very well in in-air settings and subsequently show the degradation of their performance in challenging underwater environments. The final goal of this study is to identify techniques that can improve accuracy and robustness of SLAM methods in such conditions. To achieve this goal, we investigate the potential of image enhancement techniques to improve the quality of input images used by the SLAM methods, specifically in low visibility and extreme lighting scenarios in scattering media. We present a first evaluation on calibration maneuvers and simple image restoration techniques to determine their ability to enable or enhance the performance of monocular SLAM methods in underwater environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
