# Asynchronous Multiple LiDAR-Inertial Odometry using Point-wise Inter-LiDAR Uncertainty Propagation Source: [Asynchronous Multiple LiDAR-Inertial Odometry using Point-wise Inter-LiDAR Uncertainty Propagation]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:59:40.801Z Authors: Minwoo Jung, Sangwoo Jung, Ayoung Kim Published: 2023-05-26T10:06:01.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [LiDAR-Inertial Odometry on NTU-VIRAL](): LiDAR-Inertial Odometry; NTU-VIRAL; EEE: 23.97. - [LiDAR-Inertial Odometry on Newer-College (quad-D, quad-M, quad-H, math-H)](): LiDAR-Inertial Odometry; Newer-College (quad-D, quad-M, quad-H, math-H); RMSE ATE (quad-D): 0.156. - [LiDAR-Inertial Odometry on Authors' Custom Dataset (CW, CCW, mid)](): LiDAR-Inertial Odometry; Authors' Custom Dataset (CW, CCW, mid); ATE RMSE (CCW): 27.77. - [LiDAR-Inertial Odometry on NTU-VIRAL (eee, nya, rtp, sbs, tnp, spms)](): LiDAR-Inertial Odometry; NTU-VIRAL (eee, nya, rtp, sbs, tnp, spms); ATE RMSE (m): not recorded. ## Original sources and implementation [Original paper]() [PDF]()