SE(3)-LIO: Smooth IMU Propagation With Jointly Distributed Poses on SE(3) Manifold for Accurate and Robust LiDAR-Inertial Odometry

2026-03-17

AI Summary

Existing IMU propagation methods decouple rotation and translation in motion prediction, neglecting their intrinsic coupling on the SE(3) manifold, and fail to model pose correlations in motion compensation, thereby limiting the accuracy of LiDAR-inertial odometry. This work proposes SE(3)-LIO, which, for the first time, employs a joint pose distribution on SE(3) during IMU propagation to unify the modeling of rotation and translation. Furthermore, it introduces an uncertainty-aware motion compensation (UAMC) mechanism that explicitly captures the covariance structure of relative transformations. Experimental results demonstrate that the proposed approach significantly improves pose estimation accuracy and motion distortion compensation across multiple public datasets, enhancing overall system robustness.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

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/.

Problem

• IMU propagation
• motion prediction
• motion compensation
• SE(3) manifold
• LiDAR-inertial odometry

Innovation

• SE(3) manifold
• IMU propagation
• uncertainty-aware motion compensation
• LiDAR-inertial odometry
• pose correlation