  Many LiDAR place recognition systems have been developed and tested
specifically for urban driving scenarios. Their performance in natural
environments such as forests and woodlands have been studied less closely. In
this paper, we analyzed the capabilities of four different LiDAR place
recognition systems, both handcrafted and learning-based methods, using LiDAR
data collected with a handheld device and legged robot within dense forest
environments. In particular, we focused on evaluating localization where there
is significant translational and orientation difference between corresponding
LiDAR scan pairs. This is particularly important for forest survey systems
where the sensor or robot does not follow a defined road or path. Extending our
analysis we then incorporated the best performing approach, Logg3dNet, into a
full 6-DoF pose estimation system -- introducing several verification layers
for precise registration. We demonstrated the performance of our methods in
three operational modes: online SLAM, offline multi-mission SLAM map merging,
and relocalization into a prior map. We evaluated these modes using data
captured in forests from three different countries, achieving 80% of correct
loop closures candidates with baseline distances up to 5m, and 60% up to 10m.
