RID-LIO: robust and accurate intensity-assisted LiDAR-based SLAM for degenerated environments

Abstract LiDAR-based simultaneous localization and mapping (SLAM) methods often degrade in environments lacking geometric features (such as tunnels and long corridors), where planar features are abundant but linear features are sparse. To address this issue, we propose RID-LIO, an intensity-assisted LiDAR-inertial SLAM framework that integrates adaptive intensity feature extraction and intensity-based loop detection, overcoming the reliance on geometric features in existing methods. First, 3D point clouds are cylindrically projected to generate intensity images, from which intensity line features are extracted to enhance constraints in degraded directions. A weighting function is also incorporated to optimize the quality of the pose estimation, while an efficient intensity edge context descriptor improves loop detection efficiency and reduces trajectory drift. Evaluations on the VECtor dataset show an average improvement of 63.59% in trajectory accuracy. Tests on a private dataset demonstrate that RID-LIO outperforms other state-of-the-art methods in terms of end-to-end error and map consistency.