  This paper presents a dataset, called Reeds, for research on robot perception
algorithms. The dataset aims to provide demanding benchmark opportunities for
algorithms, rather than providing an environment for testing
application-specific solutions. A boat was selected as a logging platform in
order to provide highly dynamic kinematics. The sensor package includes six
high-performance vision sensors, two long-range lidars, radar, as well as GNSS
and an IMU. The spatiotemporal resolution of sensors were maximized in order to
provide large variations and flexibility in the data, offering evaluation at a
large number of different resolution presets based on the resolution found in
other datasets. Reeds also provides means of a fair and reproducible comparison
of algorithms, by running all evaluations on a common server backend. As the
dataset contains massive-scale data, the evaluation principle also serves as a
way to avoid moving data unnecessarily.
  It was also found that naive evaluation of algorithms, where each evaluation
is computed sequentially, was not practical as the fetch and decode task of
each frame would not scale well. Instead, each frame is only decoded once and
then fed to all algorithms in parallel, including for GPU-based algorithms.
