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

Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences