  Visual localization plays an important role for intelligent robots and
autonomous driving, especially when the accuracy of GNSS is unreliable.
Recently, camera localization in LiDAR maps has attracted more and more
attention for its low cost and potential robustness to illumination and weather
changes. However, the commonly used pinhole camera has a narrow Field-of-View,
thus leading to limited information compared with the omni-directional LiDAR
data. To overcome this limitation, we focus on correlating the information of
360 equirectangular images to point clouds, proposing an end-to-end learnable
network to conduct cross-modal visual localization by establishing similarity
in high-dimensional feature space. Inspired by the attention mechanism, we
optimize the network to capture the salient feature for comparing images and
point clouds. We construct several sequences containing 360 equirectangular
images and corresponding point clouds based on the KITTI-360 dataset and
conduct extensive experiments. The results demonstrate the effectiveness of our
approach.
