雨雪天气下的激光雷达滤波算法研究

在雨雪等恶劣天气下，由于雨雪颗粒的遮挡，激光雷达的性能会受到严重影响，给三维目标检测带来了很大困难。针对这个问题，提出了一种基于马氏距离的动态离群点滤波算法；首先通过建立KD树，根据不同欧氏距离计算离群点的马氏距离，去除点云雨雪噪声；最后将该算法应用于目标检测。经过加拿大恶劣天气公开数据集(CADCD)和实际实验的验证，在中雪和大雪天气下，与DROR滤波方法对比，本文提出的滤波算法精确率分别相对提高了7.88%,7.72%;在实际雨天实验中，本文算法精确率比DROR滤波相对提高了10%。在目标检测应用方面，与仅采用Pointpillars的算法相比，采用该滤波的检测算法车辆和行人的检测精度也分别相对提高了19.26%,20.39%,在数据集和实际实验场景下均验证了本文算法的有效性。

Research on Lidar filtering algorithm for rainy and snowy weather

In bad weather such as rainy and snowy, the performance of LiDAR can be seriously affected due to the block of rain and snowflakes,which brings great difficulties to 3D target detection.Aiming at this problem, a dynamic outlier filtering algorithm based on the Mahalanobis distance is proposed. First, by establishing the KD tree, the Mahalanobis distance of outlier points is calculated to remove snowflakes noise with different Euclidean distances. After the verification of the Canadian Adverse Driving Conditions open Dataset and practical experiments, the accuracy of the filtering algorithm proposed in this paper is improved by 7.88% and 7.72% relatively, compared with the DROR filtering algorithm in medium and heavy snowy weather. In practical rainy experiments, the algorithm proposed in this study exhibited a relative improvement of 10% precision compared to the DROR filtering algorithm. For target detection applications, the detection accuracy of vehicle and pedestrian detection via the filtering algorithm is also improved by 19.26% and 20.39% relatively, compared to the algorithm using only Pointpillars,which verifies theeffectiveness of this method in dataset and real experimental scenarios.