This letter presents an accurate and robust Lidar Inertial Odometry framework. We fuse LiDAR scans with IMU data using a tightly-coupled iterative error state Kalman filter for robust and fast localization. To achieve robust correspondence matching, we represent the points as a set of Gaussian distributions and evaluate the divergence in variance for outlier rejection. Based on the fitted distributions, a new residual metric is proposed for the filter-based Lidar inertial odometry, which demonstrates an improvement from merely quantifying distance to incorporating variance disparity, further enriching the comprehensiveness and accuracy of the residual metric. Due to the strategic design of the residual metric, we propose a simple yet effective voxel-solely mapping scheme, which only necessities the maintenance of one centroid and one covariance matrix for each voxel. Experiments on different datasets demonstrate the robustness and accuracy of our framework for various data inputs and environments. To the benefit of the robotics society, we open source the code at https://github.com/Ji1Xingyu/lio_gvm.

翻译：本文提出了一种精确且鲁棒的激光雷达-惯性里程计框架。我们利用紧耦合迭代误差状态卡尔曼滤波器融合激光雷达扫描与惯性测量单元数据，以实现鲁棒且快速的定位。为实现稳健的对应匹配，我们将点云表示为高斯分布集合，并评估方差差异以剔除异常值。基于拟合分布，我们提出了一种适用于滤波型激光雷达惯性里程计的残差度量新方法，该方法从仅量化距离改进为融入方差差异，进一步提升了残差度量的全面性与准确性。得益于残差度量的策略性设计，我们提出了一种简单而有效的纯体素建图方案，该方案仅需为每个体素维护一个质心和一个协方差矩阵。在不同数据集上的实验证明了我们的框架在各种数据输入和环境下的鲁棒性与准确性。为惠及机器人学界，我们在https://github.com/Ji1Xingyu/lio_gvm 开源了代码。