  Imaging radar is an emerging sensor modality in the context of Localization
and Mapping (SLAM), especially suitable for vision-obstructed environments.
This article investigates the use of 4D imaging radars for SLAM and analyzes
the challenges in robust loop closure. Previous work indicates that 4D radars,
together with inertial measurements, offer ample information for accurate
odometry estimation. However, the low field of view, limited resolution, and
sparse and noisy measurements render loop closure a significantly more
challenging problem. Our work builds on the previous work - TBV SLAM - which
was proposed for robust loop closure with 360$^\circ$ spinning radars. This
article highlights and addresses challenges inherited from a directional 4D
radar, such as sparsity, noise, and reduced field of view, and discusses why
the common definition of a loop closure is unsuitable. By combining multiple
quality measures for accurate loop closure detection adapted to 4D radar data,
significant results in trajectory estimation are achieved; the absolute
trajectory error is as low as 0.46 m over a distance of 1.8 km, with consistent
operation over multiple environments.
