arXiv 2405.013162024-08-05cs.RO

LOG-LIO2：一种具有高效不确定性分析的激光雷达-惯性里程计

LOG-LIO2: A LiDAR-Inertial Odometry with Efficient Uncertainty Analysis

School of Surveying and Geo-Informatics, Tongji University（同济大学测量与地理信息学院）；School of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Institute of Intelligent Vehicles, Tongji University（同济大学智能车辆研究所）

Kai Huang, Junqiao Zhao, Jiaye Lin, Zhongyang Zhu, Shuangfu Song, Chen Ye, Tiantian Feng

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AI总结：

针对 LIO 中入射角影响被忽视且不确定性传播计算低效的问题，提出融合测量与表面特征的点不确定性模型及增量 Jacobian 快速传播方法，将复杂度降至 O(1)。

摘要：

激光雷达测量中的不确定性源于距离传感等因素，对 LIO（激光雷达-惯性里程计）系统至关重要，因为它会影响损失函数中的准确加权。尽管近期的 LIO 系统处理了与距离传感相关的不确定性，但入射角对不确定性的影响往往被学界忽视。此外，现有不确定性传播方法存在计算效率低下的问题。本文提出了一个综合的点不确定性模型，该模型同时考虑了来自激光雷达测量和表面特征的不确定性，并针对基于激光雷达的状态估计问题提出了一种高效的局部不确定性分析方法。我们采用投影算子，将不确定性分离到射线方向及其正交平面。随后，我们推导了特征值和特征向量相对于点的增量 Jacobian 矩阵，从而能够快速近似不确定性传播。该方法消除了对各点进行冗余遍历的需求，在加入新点时，将不确定性传播的时间复杂度从 O(n) 显著降低至 O(1)。我们在仿真和公共数据集上进行了实验，以验证所提出公式的准确性和效率。所提方法已集成到一个 LIO 系统中，系统可在 https://github.com/tiev-tongji/LOG-LIO2 获取。

英文摘要：

Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to range sensing, the impact of incident angle on uncertainty is often overlooked by the community. Moreover, the existing uncertainty propagation methods suffer from computational inefficiency. This paper proposes a comprehensive point uncertainty model that accounts for both the uncertainties from LiDAR measurements and surface characteristics, along with an efficient local uncertainty analytical method for LiDAR-based state estimation problem. We employ a projection operator that separates the uncertainty into the ray direction and its orthogonal plane. Then, we derive incremental Jacobian matrices of eigenvalues and eigenvectors w.r.t. points, which enables a fast approximation of uncertainty propagation. This approach eliminates the requirement for redundant traversal of points, significantly reducing the time complexity of uncertainty propagation from $\mathcal{O} (n)$ to $\mathcal{O} (1)$ when a new point is added. Simulations and experiments on public datasets are conducted to validate the accuracy and efficiency of our formulations. The proposed methods have been integrated into a LIO system, which is available at https://github.com/tiev-tongji/LOG-LIO2.

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