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The ability for a moving agent to localize itself in environment is the basicdemand for emerging applications, such as autonomous driving, etc. Manyexisting methods based on multiple sensors still suffer from drift. We proposea scheme that fuses map prior and vanishing points from images, which canestablish an energy term that is only constrained on rotation, called thedirection projection error. Then we embed these direction priors into avisual-LiDAR SLAM system that integrates camera and LiDAR measurements in atightly-coupled way at backend. Specifically, our method generates visualreprojection error and point to Implicit Moving Least Square(IMLS) surface ofscan constraints, and solves them jointly along with direction projection errorat global optimization. Experiments on KITTI, KITTI-360 and Oxford RadarRobotcar show that we achieve lower localization error or Absolute Pose Error(APE) than prior map, which validates our method is effective.

JVLDLoc: a Joint Optimization of Visual-LiDAR Constraints and Direction Priors for Localization in Driving Scenario