In recent years, multiple Light Detection and Ranging (LiDAR) systems have grown in popularity due to their enhanced accuracy and stability from the increased field of view (FOV). However, integrating multiple LiDARs can be challenging, attributable to temporal and spatial discrepancies. Common practice is to transform points among sensors while requiring strict time synchronization or approximating transformation among sensor frames. Unlike existing methods, we elaborate the inter-sensor transformation using continuous-time (CT) inertial measurement unit (IMU) modeling and derive associated ambiguity as a point-wise uncertainty. This uncertainty, modeled by combining the state covariance with the acquisition time and point range, allows us to alleviate the strict time synchronization and to overcome FOV difference. The proposed method has been validated on both public and our datasets and is compatible with various LiDAR manufacturers and scanning patterns. We open-source the code for public access at https://github.com/minwoo0611/MA-LIO.

翻译：近年来，多激光雷达系统因视场角扩大带来的精度与稳定性提升而备受关注。然而，时间与空间差异给多雷达集成带来挑战。现有方法通常需严格时间同步或近似传感器帧间变换，而本文通过连续时间惯性测量单元建模阐述传感器间变换，并导出相关模糊性作为逐点不确定性。该不确定性将状态协方差与采集时间及点距离相结合，可缓解严格时间同步要求并克服视场差异。所提方法已在公开数据集及自建数据集上完成验证，且兼容不同激光雷达制造商与扫描模式。相关代码已开源发布于https://github.com/minwoo0611/MA-LIO。