  Light-weight camera localization in existing maps is essential for
vision-based navigation. Currently, visual and visual-inertial odometry
(VO\&VIO) techniques are well-developed for state estimation but with
inevitable accumulated drifts and pose jumps upon loop closure. To overcome
these problems, we propose an efficient monocular camera localization method in
prior LiDAR maps using direct 2D-3D line correspondences. To handle the
appearance differences and modality gaps between LiDAR point clouds and images,
geometric 3D lines are extracted offline from LiDAR maps while robust 2D lines
are extracted online from video sequences. With the pose prediction from VIO,
we can efficiently obtain coarse 2D-3D line correspondences. Then the camera
poses and 2D-3D correspondences are iteratively optimized by minimizing the
projection error of correspondences and rejecting outliers. Experimental
results on the EurocMav dataset and our collected dataset demonstrate that the
proposed method can efficiently estimate camera poses without accumulated
drifts or pose jumps in structured environments.
