  Enabling autonomous robots to operate robustly in challenging environments is
necessary in a future with increased autonomy. For many autonomous systems,
estimation and odometry remains a single point of failure, from which it can
often be difficult, if not impossible, to recover. As such robust odometry
solutions are of key importance. In this work a method for tightly-coupled
LiDAR-Radar-Inertial fusion for odometry is proposed, enabling the mitigation
of the effects of LiDAR degeneracy by leveraging a complementary perception
modality while preserving the accuracy of LiDAR in well-conditioned
environments. The proposed approach combines modalities in a factor graph-based
windowed smoother with sensor information-specific factor formulations which
enable, in the case of degeneracy, partial information to be conveyed to the
graph along the non-degenerate axes. The proposed method is evaluated in
real-world tests on a flying robot experiencing degraded conditions including
geometric self-similarity as well as obscurant occlusion. For the benefit of
the community we release the datasets presented:
https://github.com/ntnu-arl/lidar_degeneracy_datasets.
