  In this paper, we address the lack of datasets for - and the issue of
reproducibility in - collaborative SLAM pose graph optimizers by providing a
novel pose graph generator. Our pose graph generator, kollagen, is based on a
random walk in a planar grid world, similar to the popular M3500 dataset for
single agent SLAM. It is simple to use and the user can set several parameters,
e.g., the number of agents, the number of nodes, loop closure generation
probabilities, and standard deviations of the measurement noise. Furthermore, a
qualitative execution time analysis of our pose graph generator showcases the
speed of the generator in the tunable parameters.
  In addition to the pose graph generator, our paper provides two example
datasets that researchers can use out-of-the-box to evaluate their algorithms.
One of the datasets has 8 agents, each with 3500 nodes, and 67645 constraints
in the pose graphs, while the other has 5 agents, each with 10000 nodes, and
76134 constraints. In addition, we show that current state-of-the-art pose
graph optimizers are able to process our generated datasets and perform pose
graph optimization.
  The data generator can be found at
https://github.com/EricssonResearch/kollagen.

Robotics