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