LIO-GVM：一种基于高斯体素地图的精确紧耦合激光雷达-惯性里程计
LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map
AI总结：
本文提出LIO-GVM，一种基于高斯体素地图的紧耦合激光雷达-惯性里程计，通过高斯分布残差度量实现鲁棒匹配与精确建图，并在多数据集上验证了其鲁棒性与准确性。
摘要：
本文提出了一种精确且鲁棒的激光雷达惯性里程计框架。我们采用紧耦合的迭代误差状态卡尔曼滤波器融合激光雷达扫描与IMU数据，以实现鲁棒且快速的定位。为实现鲁棒的对应匹配，我们将点表示为一系列高斯分布，并通过评估方差散度来进行外点剔除。基于拟合的分布，我们为基于滤波器的激光雷达惯性里程计提出了一种新的残差度量，该度量展示了从仅量化距离到纳入方差差异的改进，进一步丰富了残差度量的全面性和准确性。由于残差度量的策略性设计，我们提出了一种简单而有效的仅体素建图方案，该方案只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对于各种数据输入和环境的鲁棒性和准确性。为了惠及机器人学界，我们在https://github.com/Ji1Xingyu/lio_gvm开源了代码。
英文摘要：
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.
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