  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.
