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，多激光雷达系统因其通过增加视场角而提升的精度和稳定性而日益普及。然而，由于时空差异，集成多个激光雷达存在挑战。常规做法是在传感器间进行点云变换，同时要求严格时间同步或近似传感器坐标系间的变换。与现有方法不同，我们利用连续时间惯性测量单元建模详细阐述了传感器间变换，并将相关歧义推导为逐点不确定性。这种不确定性通过结合状态协方差与采集时间和点距离进行建模，使我们能够放宽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和我们自己的数据集上得到验证，且兼容多种激光雷达制造商和扫描模式。我们在https://github.com/minwoo0611/MA-LIO开源了代码。