  Many robotic tasks rely on the accurate localization of moving objects within
a given workspace. This information about the objects' poses and velocities are
used for control,motion planning, navigation, interaction with the environment
or verification. Often motion capture systems are used to obtain such a state
estimate. However, these systems are often costly, limited in workspace size
and not suitable for outdoor usage. Therefore, we propose a lightweight and
easy to use, visual-inertial Simultaneous Localization and Mapping approach
that leverages cost-efficient, paper printable artificial landmarks, socalled
fiducials. Results show that by fusing visual and inertial data, the system
provides accurate estimates and is robust against fast motions and changing
lighting conditions. Tight integration of the estimation of sensor and fiducial
pose as well as extrinsics ensures accuracy, map consistency and avoids the
requirement for precalibration. By providing an open source implementation and
various datasets, partially with ground truth information, we enable community
members to run, test, modify and extend the system either using these datasets
or directly running the system on their own robotic setups.
