# SE(3)-LIO: Smooth IMU Propagation With Jointly Distributed Poses on SE(3) Manifold for Accurate and Robust LiDAR-Inertial Odometry Source: [SE(3)-LIO: Smooth IMU Propagation With Jointly Distributed Poses on SE(3) Manifold for Accurate and Robust LiDAR-Inertial Odometry]() Collection index: [llms.txt]() Data updated: 2026-03-19T05:00:05.030Z Authors: Gunhee Shin, Seungjae Lee, Jei Kong, Youngwoo Seo, Hyun Myung Published: 2026-03-17T04:55:23.000Z ## Abstract In estimating odometry accurately, an inertial measurement unit (IMU) is widely used owing to its high-rate measurements, which can be utilized to obtain motion information through IMU propagation. In this paper, we address the limitations of existing IMU propagation methods in terms of motion prediction and motion compensation. In motion prediction, the existing methods typically represent a 6-DoF pose by separating rotation and translation and propagate them on their respective manifold, so that the rotational variation is not effectively incorporated into translation propagation. During motion compensation, the relative transformation between predicted poses is used to compensate motion-induced distortion in other measurements, while inherent errors in the predicted poses introduce uncertainty in the relative transformation. To tackle these challenges, we represent and propagate the pose on SE(3) manifold, where propagated translation properly accounts for rotational variation. Furthermore, we precisely characterize the relative transformation uncertainty by considering the correlation between predicted poses, and incorporate this uncertainty into the measurement noise during motion compensation. To this end, we propose a LiDAR-inertial odometry (LIO), referred to as SE(3)-LIO, that integrates the proposed IMU propagation and uncertainty-aware motion compensation (UAMC). We validate the effectiveness of SE(3)-LIO on diverse datasets. Our source code and additional material are available at: https://se3-lio.github.io/. ## 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 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): 0.128. - [LiDAR-Inertial Odometry on Authors' Custom Dataset (CW, CCW, mid)](): LiDAR-Inertial Odometry; Authors' Custom Dataset (CW, CCW, mid); ATE RMSE (CCW): 15.86. - [LiDAR-Inertial Odometry on NTU-VIRAL](): LiDAR-Inertial Odometry; NTU-VIRAL; EEE: 26.66. - [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]()