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In this letter, we propose a semantics-enhanced solid-state-LiDAR-inertialodometry (SE-LIO) in tree-rich environments. Multiple LiDAR frames are firstmerged and compensated with the inertial navigation system (INS) to increasethe point-cloud coverage, thus improving the accuracy of semantic segmentation.The unstructured point clouds, such as tree leaves and dynamic objects, arethen removed with the semantic information. Furthermore, the pole-like pointclouds, primarily tree trunks, are modeled as cylinders to improve positioningaccuracy. An adaptive piecewise cylinder-fitting method is proposed toaccommodate environments with a high prevalence of curved tree trunks. Finally,the iterated error-state Kalman filter (IESKF) is employed for stateestimation. Point-to-cylinder and point-to-plane constraints are tightlycoupled with the prior constraints provided by the INS to obtain the maximum aposteriori estimation. Targeted experiments are conducted in complex campus andpark environments to evaluate the performance of SE-LIO. The proposed methods,including removing the unstructured point clouds and the adaptive cylinderfitting, yield improved accuracy. Specifically, the positioning accuracy of theproposed SE-LIO is improved by 43.1% compared to the plane-based LIO.

SE-LIO: Semantics-enhanced Solid-State-LiDAR-Inertial Odometry for Tree-rich Environments