Efficient Visual-Inertial Navigation with Point-Plane Map

Accurate and real-time global pose estimation relative to a global prior map is indispensable in many applications, such as logistics with micro aerial vehicles and Augmented Reality. Supposed that a pure sparse 3D point map can provide a structureless representation of the environment, then generating a point-plane prior map can further model the environment topology and offer global constraints for an accurate localization. To implement this, we propose a filter-based, large-scale visual-inertial odometry system, termed PPM-VIO, which utilizes a point-plane map to correct the cumulative drift. Our system, detecting coplanar information from sparse point clouds with semantic information, achieves accurate online plane matching via geometric constraints, semantic constraints, and descriptor constraints. To improve the localization performance, we effectively integrate and formulate the global planar measurements and points measurements in a filter-based estimator. The effectiveness of the proposed method is extensively validated on real-world datasets collected in different scenarios. Experimental results demonstrate that, rather than using the point map alone, leveraging the plane information in the prior map can yield better trajectory estimates and broaden the effective scope of the prior map in different scenes.