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).

翻译：里程计估计对于每个需要在未知环境中导航的自主系统至关重要。在现代移动机器人中，3D LiDAR-惯性系统常被用于此任务。通过融合LiDAR扫描和IMU测量，这些系统能够减少因连续配准各LiDAR扫描而产生的累积漂移，并提供鲁棒的姿态估计。尽管有效，LiDAR-惯性里程计系统需要适当的参数调优才能部署。在本文中，我们提出LIO-EKF，一个基于点到点配准和经典扩展卡尔曼滤波器方案的紧耦合LiDAR-惯性里程计系统。我们提出一种自适应数据关联方法，该方法考虑了相对姿态不确定性、地图离散化误差以及LiDAR噪声。通过这种方式，我们能够大幅减少针对特定环境类型需要调优的参数。实验评估表明，所提系统性能与当前最先进的LiDAR-惯性里程计算法相当，但在计算里程计时速度显著更快。我们实现的源代码已公开（https://github.com/YibinWu/LIO-EKF）。