SE(3)-LIO：在SE(3)流形上联合分布姿态的平滑IMU传播以实现准确且鲁棒的LiDAR-惯性里程计
SE(3)-LIO: Smooth IMU Propagation With Jointly Distributed Poses on SE(3) Manifold for Accurate and Robust LiDAR-Inertial Odometry
AI总结：
本文提出SE(3)-LIO，通过在SE(3)流形上联合分布姿态的IMU传播和不确定性感知运动补偿，解决现有方法在运动预测和补偿中的不足，提升LiDAR-惯性里程计的准确性与鲁棒性。
摘要：
在准确估计里程计方面，惯性测量单元（IMU）因其高采样率而被广泛使用，可通过IMU传播获取运动信息。本文针对现有IMU传播方法在运动预测和运动补偿中的局限性进行改进。在运动预测中，现有方法通常将6自由度姿态分离为旋转和平移，并分别在各自流形上传播，导致旋转变化未有效融入平移传播。在运动补偿中，预测姿态之间的相对变换用于补偿其他测量中的运动诱导畸变，但预测姿态的固有误差会引入相对变换的不确定性。为解决这些问题，我们表示并传播姿态于SE(3)流形上，其中传播的平移适当考虑了旋转变化。此外，我们通过考虑预测姿态之间的相关性，精确表征相对变换的不确定性，并将其纳入运动补偿中的测量噪声。为此，我们提出了一种LiDAR-惯性里程计（LIO），称为SE(3)-LIO，集成了所提出的IMU传播和不确定性感知运动补偿（UAMC）。我们验证了SE(3)-LIO在多种数据集上的有效性。我们的源代码和附加材料可在：https://se3-lio.github.io/上获得。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
