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鲁棒的激光雷达-惯性里程计框架。我们采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IMU数据，以实现鲁棒快速的定位。为达成鲁棒对应匹配，我们通过一组高斯分布表征点云，并利用方差差异判别离群点。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新残差度量——该度量从单纯的量化距离提升为融合方差差异，进一步丰富了残差度量的全面性与精确性。得益于残差度量的策略性设计，我们提出了一种简洁高效的仅体素建图方案，每个体素仅需维护一个质心与一个协方差矩阵。在多个数据集上的实验表明，本框架在多样化数据输入与环境中均具有鲁棒性与精确性。为惠及机器人学界，我们在https://github.com/Ji1Xingyu/lio_gvm 开源了代码。