  Deep-sea robot operations demand a high level of safety, efficiency and
reliability. As a consequence, measures within the development stage have to be
implemented to extensively evaluate and benchmark system components ranging
from data acquisition, perception and localization to control. We present an
approach based on high-fidelity simulation that embeds spatial and
environmental conditions from recorded real-world data. This simulation in the
loop (SIL) methodology allows for mitigating the discrepancy between simulation
and real-world conditions, e.g. regarding sensor noise. As a result, this work
provides a platform to thoroughly investigate and benchmark behaviors of system
components concurrently under real and simulated conditions. The conducted
evaluation shows the benefit of the proposed work in tasks related to
perception and self-localization under changing spatial and environmental
conditions.
