  Localization has been a challenging task for autonomous navigation. A loop
detection algorithm must overcome environmental changes for the place
recognition and re-localization of robots. Therefore, deep learning has been
extensively studied for the consistent transformation of measurements into
localization descriptors. Street view images are easily accessible; however,
images are vulnerable to appearance changes. LiDAR can robustly provide precise
structural information. However, constructing a point cloud database is
expensive, and point clouds exist only in limited places. Different from
previous works that train networks to produce shared embedding directly between
the 2D image and 3D point cloud, we transform both data into 2.5D depth images
for matching. In this work, we propose a novel cross-matching method, called
(LC)$^2$, for achieving LiDAR localization without a prior point cloud map. To
this end, LiDAR measurements are expressed in the form of range images before
matching them to reduce the modality discrepancy. Subsequently, the network is
trained to extract localization descriptors from disparity and range images.
Next, the best matches are employed as a loop factor in a pose graph. Using
public datasets that include multiple sessions in significantly different
lighting conditions, we demonstrated that LiDAR-based navigation systems could
be optimized from image databases and vice versa.
