SE-LIO：面向树木丰富环境的语义增强固态激光雷达-惯性里程计
SE-LIO: Semantics-enhanced Solid-State-LiDAR-Inertial Odometry for Tree-rich Environments
AI总结：
本文提出SE-LIO，通过语义分割去除非结构化点云、对树干进行自适应分段圆柱拟合，并与INS先验约束紧耦合，在树木丰富环境中将定位精度较平面LIO提升43.1%。
摘要：
本文提出了一种面向树木丰富环境的语义增强固态激光雷达-惯性里程计（SE-LIO）。首先，将多帧激光雷达点云进行合并，并利用惯性导航系统（INS）进行运动补偿，以增大点云覆盖范围，从而提高语义分割的精度。随后，利用语义信息去除非结构化点云，例如树叶和动态物体。此外，将主要包含树干的杆状点云建模为圆柱体，以提高定位精度。针对弯曲树干普遍存在的环境，提出了一种自适应分段圆柱拟合方法。最后，采用迭代误差状态卡尔曼滤波器（IESKF）进行状态估计。点-圆柱约束和点-平面约束与INS提供的先验约束紧密耦合，以获得最大后验估计。在复杂的校园和公园环境中开展了针对性实验，以评估SE-LIO的性能。所提出的方法，包括去除非结构化点云和自适应圆柱拟合，均带来了精度提升。具体而言，与基于平面的LIO相比，所提出的SE-LIO的定位精度提高了43.1%。
英文摘要：
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.
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