For most LiDAR-inertial odometry, accurate initial state, including temporal offset and extrinsic transformation between LiDAR and 6-axis IMUs, play a significant role and are often considered as prerequisites. However, such information may not be always available in customized LiDAR-inertial systems. In this paper, we propose a full and online LiDAR-inertial system initialization process that calibrates the temporal offset and extrinsic parameter between LiDARs and IMUs, and also the gravity vector and IMU bias by aligning the state estimated from LiDAR measurements with that measured by IMU. We implement the proposed method as an initialization module, which, if enabled, automatically detects the degree of excitation of the collected data and calibrate, on-the-fly, the temporal offset, extrinsic, gravity vector, and IMU bias, which are then used as high-quality initial state values for online LiDAR-inertial odometry systems. Experiments conducted with different types of LiDARs and LiDAR-inertial combinations show the robustness, adaptability and efficiency of our initialization method. The implementation of our LiDAR-inertial initialization procedure and test data are open-sourced on Github and also integrated into a state-of-the-art LiDAR-inertial odometry system FAST-LIO2.

翻译：对于大多数LiDAR-肾上腺测量,准确的初始状态,包括LIDAR和6-Axis IMUS之间的时间偏移和外部变异,对于大多数LIDAR-肾上腺测量,准确的初始状态,包括时间偏移和LIDAR和6-Axis IMUS之间的外部变异,可以发挥重要作用,而且常常被视为先决条件。然而,在本文中,我们提出一个完整和在线的LIDAR-肾上腺系统初始化进程,以校准LIDAR和IMU之间的时间偏差,同时调整重力矢量和IMU的偏差,将LIDAR测量结果的估算结果与IMU测量结果相匹配。我们将拟议方法作为初始化模块,如果启用,将自动检测所收集的数据和校准的程度,即时校准、时间偏、外偏移、重力矢量矢量和IMU的偏差,然后用作在线LDAR-肾上LDAR-DI-DI2号测量系统的高级初始化和初步测试程序的效率。