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This paper introduces SE(3)-LIO, a LiDAR-inertial odometry method that propagates poses on the SE(3) manifold to better account for rotational variations in translation propagation. It also incorporates uncertainty-aware motion compensation (UAMC) by characterizing the correlation between predicted poses and integrating this uncertainty into the measurement noise. Experiments on diverse datasets demonstrate the effectiveness of SE(3)-LIO in improving odometry accuracy and robustness.
By propagating poses directly on the SE(3) manifold and carefully modeling pose uncertainty, SE(3)-LIO achieves more accurate and robust LiDAR-inertial odometry.
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/.
