发表机构
University of Manchester; University of Edinburgh（曼彻斯特大学; 爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有LIO系统未充分利用局部地面几何约束的问题，提出GR-LIO框架，通过将机体到地面高度参数化的局部地面平面纳入滤波状态估计，实现高效地面分割与约束更新，在公开及自采数据集上显著提升定位精度与计算效率。
AI 中文摘要
激光雷达-惯性里程计（LIO）被广泛用于地面自主移动机器人的状态估计。然而，现有LIO系统中局部地面表面提供的几何约束在很大程度上未被充分利用。本文提出了一种基于滤波器的局部地面感知LIO框架，该框架将局部地面平面几何显式地纳入状态估计过程，以提高定位精度和计算效率。具体而言，局部地面平面通过机器人姿态和机体到地面（B-G）高度进行参数化，并在状态估计过程中连续传播。基于所提出的B-G几何模型，传播的局部地面平面能够实现高效且可靠的地面分割。分割后的地面点随后通过点到平面几何约束被纳入滤波器更新中，从而提高状态估计的精度和效率。此外，引入平面运动更新，利用传播的局部地面平面作为额外的几何约束，有效抑制垂直漂移并提高估计鲁棒性。为解决初始未知的B-G高度，开发了一种高效的初始化策略，随后进行在线标定过程以持续细化。所提出的系统在多个公开基准数据集和自采集的真实世界数据集上进行了评估，涵盖多种操作场景。实验结果表明，所提出的方法在定位精度和计算效率方面均持续优于代表性的LIO方法。
英文摘要
LiDAR-inertial odometry (LIO) is widely used for state estimation in ground-based autonomous mobile robots. However, the geometric constraints provided by the local ground surface remain largely underexploited in existing LIO systems. This paper proposes a filter-based local ground-aware LIO framework that explicitly incorporates local ground plane geometry into the state estimation process to improve both localization accuracy and computational efficiency. Specifically, a local ground plane is parameterized by the robot orientation and the body-to-ground (B-G) height and continuously propagated within the state estimation process. Based on the proposed B-G geometry model, a propagated local ground plane enables efficient and reliable ground segmentation. The segmented ground points are then incorporated into the filter update through point-to-plane geometric constraints, improving both state estimation accuracy and efficiency. Furthermore, a planar motion update is introduced to exploit the propagated local ground plane as an additional geometric constraint, effectively suppressing vertical drift and improving estimation robustness. To address the initially unknown B-G height, an efficient initialization strategy is developed, followed by an online calibration procedure for continuous refinement. The proposed system is evaluated on several public benchmark datasets and self-collected real-world datasets covering diverse operating scenarios. Experimental results demonstrate that the proposed method consistently outperforms representative LIO methods in terms of both localization accuracy and computational efficiency.
Comments15 pages Journal
