arXiv 2412.058732026-06-16cs.RO

AC-LIO：通过选择性帧内平滑实现激光雷达-惯性里程计中畸变的渐近补偿

AC-LIO: Towards Asymptotic Compensation for Distortion in LiDAR-Inertial Odometry via Selective Intra-Frame Smoothing

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, China（信息工程测绘遥感国家重点实验室（LIESMARS），武汉大学，中国）；Department of Mechanical Engineering, University of Michigan-Dearborn（密歇根大学迪尔伯恩分校机械工程系）；Electronic Information School, Wuhan University, China（武汉大学电子信息学院，中国）；School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子电气工程学院，新加坡）

Tianxiang Zhang, Xuanxuan Zhang, Wenlei Fan, Xin Xia, Huai Yu, Lin Wang, You Li

备注：: 11 pages, 9 figures

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

提出AC-LIO框架，通过渐近反向传播更新项并在收敛准则指导下补偿残余运动畸变，以最小计算开销提升离散状态LIO系统精度，在长期大尺度定位中平均RMSE降低30.4%。

摘要：

现有的激光雷达-惯性里程计（LIO）方法通常利用IMU积分得到的先验轨迹来补偿激光雷达帧内的运动畸变。然而，先验轨迹与真实轨迹之间的差异会导致残余运动畸变，破坏激光雷达帧与其对应几何环境的一致性。这种不平衡可能导致点云注册陷入局部最优，从而在长期和大尺度定位中加剧漂移。为此，我们提出了一种新颖的LIO框架，称为AC-LIO，采用选择性帧内平滑。我们的核心思想是在收敛准则的指导下渐近反向传播当前更新项并补偿残余运动畸变，旨在以最小的计算开销提高离散状态LIO系统的精度。大量实验表明，与现有技术相比，我们的AC-LIO框架进一步提升了里程计精度，平均RMSE比第二名结果降低约30.4%，从而显著提高了长期和大尺度定位与建图的精度。

英文摘要：

Existing LiDAR-Inertial Odometry (LIO) methods typically utilize the prior trajectory derived from the IMU integration to compensate for the motion distortion within LiDAR frames. However, discrepancies between the prior and true trajectory can lead to residual motion distortions that compromise the consistency of LiDAR frame with its corresponding geometric environment. This imbalance may result in pointcloud registration becoming trapped in local optima, thereby exacerbating drift during long-term and large-scale localization. To this end, we propose a novel LIO framework with selective intra-frame smoothing dubbed AC-LIO. Our core idea is to asymptotically backpropagate current update term and compensate for residual motion distortion under the guidance of convergence criteria, aiming to improve the accuracy of discrete-state LIO system with minimal computational increase. Extensive experiments demonstrate that our AC-LIO framework further enhances odometry accuracy compared to prior arts, with about 30.4% reduction in average RMSE over the second best result, leading to marked improvements in the accuracy of long-term and large-scale localization and mapping.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。