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.

翻译：本文对用于水下机器人的单目视觉同步定位与建图方法所面临的挑战进行了全面研究。尽管过去十年中，利用视觉数据的状态估计方法取得了显著进展，但大多数评估仅限于可控的室内和城市环境，并在这些环境中展示了令人瞩目的性能。然而，这些技术尚未在极端挑战性条件下得到广泛测试，例如水下场景——其中水况、光照条件、机器人路径和深度等因素会极大地影响算法性能。因此，我们的评估在真实的AUV场景以及能够提供精确外部参考的实验室环境中进行。重点在于理解环境条件（如水的光学特性和光照场景）对单目vSLAM方法性能的影响。为此，我们首先展示所有方法在空气中表现极佳，随后揭示其在挑战性水下环境中性能的退化。本研究的最终目标是识别能够在此类条件下提升SLAM方法精度与鲁棒性的技术。为实现这一目标，我们探究了图像增强技术改善SLAM方法输入图像质量的潜力，特别是在散射介质中低可见度和极端光照场景下。我们首次对校准操作和简单图像恢复技术进行了评估，以确定它们在使单目SLAM方法能够在增强水下环境中发挥作用或提升其性能的能力。