  Place recognition is indispensable for a drift-free localization system. Due
to the variations of the environment, place recognition using single-modality
has limitations. In this paper, we propose a bi-modal place recognition method,
which can extract a compound global descriptor from the two modalities, vision
and LiDAR. Specifically, we first build the elevation image generated from 3D
points as a structural representation. Then, we derive the correspondences
between 3D points and image pixels that are further used in merging the
pixel-wise visual features into the elevation map grids. In this way, we fuse
the structural features and visual features in the consistent bird-eye view
frame, yielding a semantic representation, namely CORAL. And the whole network
is called CORAL-VLAD. Comparisons on the Oxford RobotCar show that CORAL-VLAD
has superior performance against other state-of-the-art methods. We also
demonstrate that our network can be generalized to other scenes and sensor
configurations on cross-city datasets.
