  LiDAR-based localization and mapping is one of the core components in many
modern robotic systems due to the direct integration of range and geometry,
allowing for precise motion estimation and generation of high quality maps in
real-time. Yet, as a consequence of insufficient environmental constraints
present in the scene, this dependence on geometry can result in localization
failure, happening in self-symmetric surroundings such as tunnels. This work
addresses precisely this issue by proposing a neural network-based estimation
approach for detecting (non-)localizability during robot operation. Special
attention is given to the localizability of scan-to-scan registration, as it is
a crucial component in many LiDAR odometry estimation pipelines. In contrast to
previous, mostly traditional detection approaches, the proposed method enables
early detection of failure by estimating the localizability on raw sensor
measurements without evaluating the underlying registration optimization.
Moreover, previous approaches remain limited in their ability to generalize
across environments and sensor types, as heuristic-tuning of degeneracy
detection thresholds is required. The proposed approach avoids this problem by
learning from a collection of different environments, allowing the network to
function over various scenarios. Furthermore, the network is trained
exclusively on simulated data, avoiding arduous data collection in challenging
and degenerate, often hard-to-access, environments. The presented method is
tested during field experiments conducted across challenging environments and
on two different sensor types without any modifications. The observed detection
performance is on par with state-of-the-art methods after environment-specific
threshold tuning.
