LIO-EKF：使用扩展卡尔曼滤波器的高频激光雷达-惯性里程计
LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters
AI总结：
本文提出紧耦合LIO-EKF激光雷达-惯性里程计系统，利用点到点配准、扩展卡尔曼滤波和考虑不确定性的自适应数据关联减少参数调优，在保持先进精度的同时显著提升计算速度。
摘要：
里程计估计对于每个需要在未知环境中导航的自主系统都至关重要。在现代移动机器人中，3D激光雷达-惯性系统经常被用于这一任务。通过融合激光雷达扫描和IMU测量，这些系统能够减少由顺序配准单帧激光雷达扫描所导致的累积漂移，并提供鲁棒的位姿估计。尽管有效，但激光雷达-惯性里程计系统在部署时需要适当的参数调优。本文提出LIO-EKF，这是一个基于点到点配准和经典扩展卡尔曼滤波框架的紧耦合激光雷达-惯性里程计系统。我们提出了一种自适应数据关联方法，该方法考虑相对位姿不确定性、地图离散化误差以及激光雷达噪声。通过这种方式，我们可以大幅减少针对给定环境类型所需调优的参数。实验评估表明，所提出的系统在性能上与最先进的激光雷达-惯性里程计流程相当，但在计算里程计时明显更快。我们实现的源代码已公开发布（https://github.com/YibinWu/LIO-EKF）。
英文摘要：
Odometry estimation is crucial for every autonomous system requiring navigation in an unknown environment. In modern mobile robots, 3D LiDAR-inertial systems are often used for this task. By fusing LiDAR scans and IMU measurements, these systems can reduce the accumulated drift caused by sequentially registering individual LiDAR scans and provide a robust pose estimate. Although effective, LiDAR-inertial odometry systems require proper parameter tuning to be deployed. In this paper, we propose LIO-EKF, a tightly-coupled LiDAR-inertial odometry system based on point-to-point registration and the classical extended Kalman filter scheme. We propose an adaptive data association that considers the relative pose uncertainty, the map discretization errors, and the LiDAR noise. In this way, we can substantially reduce the parameters to tune for a given type of environment. The experimental evaluation suggests that the proposed system performs on par with the state-of-the-art LiDAR-inertial odometry pipelines but is significantly faster in computing the odometry. The source code of our implementation is publicly available (https://github.com/YibinWu/LIO-EKF).
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。
