  The success of re-localisation has crucial implications for the practical
deployment of robots operating within a prior map or relative to one another in
real-world scenarios. Using single-modality, place recognition and localisation
can be compromised in challenging environments such as forests. To address
this, we propose a strategy to prevent lidar-based re-localisation failure
using lidar-image cross-modality. Our solution relies on self-supervised 2D-3D
feature matching to predict alignment and misalignment. Leveraging a deep
network for lidar feature extraction and relative pose estimation between point
clouds, we train a model to evaluate the estimated transformation. A model
predicting the presence of misalignment is learned by analysing image-lidar
similarity in the embedding space and the geometric constraints available
within the region seen in both modalities in Euclidean space. Experimental
results using real datasets (offline and online modes) demonstrate the
effectiveness of the proposed pipeline for robust re-localisation in
unstructured, natural environments.
