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

TL;DR

SE-LIO introduces a semantics-enhanced LiDAR-inertial odometry system that improves positioning accuracy in tree-rich environments by merging frames, removing unstructured points, modeling tree trunks as cylinders, and employing advanced filtering.

Contribution

This work presents a novel semantics-enhanced approach combining semantic segmentation, adaptive cylinder fitting, and error-state Kalman filtering for improved odometry in complex outdoor environments.

Findings

Positioning accuracy improved by 43.1% over plane-based LIO.

Effective removal of unstructured point clouds enhances accuracy.

Adaptive cylinder fitting accommodates curved tree trunks.

Abstract

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.