  Current global re-localization algorithms are built on top of localization
and mapping methods andheavily rely on scan matching and direct point cloud
feature extraction and therefore are vulnerable infeatureless demanding
environments like caves and tunnels. In this article, we propose a novel
globalre-localization framework that: a) does not require an initial guess,
like most methods do, while b)it has the capability to offer the
top-kcandidates to choose from and last but not least provides anevent-based
re-localization trigger module for enabling, and c) supporting completely
autonomousrobotic missions. With the focus on subterranean environments with
low features, we opt to usedescriptors based on range images from 3D LiDAR
scans in order to maintain the depth informationof the environment. In our
novel approach, we make use of a state-of-the-art data-driven
descriptorextraction framework for place recognition and orientation regression
and enhance it with the additionof a junction detection module that also
utilizes the descriptors for classification purposes.
