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.