  Despite the advances in robotics a large proportion of the of parts handling
tasks in the automotive industry's internal logistics are not automated but
still performed by humans. A key component to competitively automate these
processes is a 6D pose estimation that can handle a large number of different
parts, is adaptable to new parts with little manual effort, and is sufficiently
accurate and robust with respect to industry requirements. In this context, the
question arises as to the current status quo with respect to these measures. To
address this we built a representative 6D pose estimation pipeline with
state-of-the-art components from economically scalable real to synthetic data
generation to pose estimators and evaluated it on automotive parts with regards
to a realistic sequencing process. We found that using the data generation
approaches, the performance of the trained 6D pose estimators are promising,
but do not meet industry requirements. We reveal that the reason for this is
the inability of the estimators to provide reliable uncertainties for their
poses, rather than the ability of to provide sufficiently accurate poses. In
this context we further analyzed how RGB- and RGB-D-based approaches compare
against this background and show that they are differently vulnerable to the
domain gap induced by synthetic data.
