Investigation of the Challenges of Underwater-Visual-Monocular-SLAM
Michele Grimaldi David Nakath Mengkun She Kevin Köser
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.