基于卡尔曼融合的激光雷达特征匹配方法

针对现有激光雷达特征匹配算法线段特征匹配定位不够准确的问题，提出了一种基于卡尔曼融合的激光雷达特征匹配算法。首先扫描一帧雷达数据，利用改进的激光雷达线段特征提取方法，提取出特征线段，得到局部地图。接着确定局部地图旋转、平移参数，再将局部与全局地图进行匹配，根据相对偏差得到匹配结果。然后基于卡尔曼，利用IMU数据作下一时刻的预测估计，利用激光雷达匹配结果作观测，两者融合得到最优估计。实验结果表明该方法相对现有特征匹配算法在特征线段的匹配准确性上更高，因此定位导航的精度和鲁棒性也更好。

The segment feature matching method for LiDAR based on Kalman fusion

In this paper,a feature matching method for LiDAR based on Kalman fusion is proposed to address the problem of inaccurate localization using line segment features in existing LiDAR feature matching algorithms. Firstly,a frame of LiDAR data is scanned,and local map is generated by using an improved method for extracting line segment features. The rotation and translation parameters of the partial map are then determined,and the partial map is matched with the global map to obtain the matching result according to the relative deviation. Then,based on the Kalman filter,the IMU data is used to predict the estimation for the next moment,and the LiDAR matching result is used as the observation. Finally,the two results are fused to obtain the optimal estimation. The experimental results show that this method is more accurate in matching line segment features compared to the existing feature matching algorithms,which leads to better precision and robustness in localization and navigation.