Abstract:We propose RLP-VIO-a robust and lightweight monocular visual-inertial odometry system using multiplane priors. With planes extracted from the point cloud, visual-inertial-plane PnP uses the plane information for fast localization. Depth estimation is susceptible to degenerated motion, so the planes are expanded in a reprojection consensus-based way robust to depth errors.For sensor fusion, our sliding-window optimization uses a novel structureless plane-distance error cost, which prevents the fill-in effect th… Show more
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