Adaptive random sample consensus method for ground filtering of airborne LiDAR

Abstract

Ground filtering is an essential step in the comprehensive processing of airborne LiDAR point clouds. However, the performances of existing ground filtering algorithms are usually affected by manual thresholds, and many algorithms have high complexity and are not suitable for applications with high timeliness requirements. In this paper, a fast ground filtering algorithm for airborne point clouds based on Random Sample Consensus (RANSAC) and adaptive threshold acquisition is proposed. Statistical filtering algorithm is used to filter out outliers and abnormal points based on the Z-scale sequence of the point clouds, the threshold is adaptively obtained according to the filtered Z-scale sequence, and then the adaptive threshold and RANSAC are combined to achieve rapid ground filtering. The proposed algorithm is used to perform threshold calculation and ground filtering on point clouds dataset and real-world point clouds, the results show that the adaptively obtained threshold is located in the optimal threshold interval, and the algorithm in this paper can extract ground points quickly and with small errors. The proposed method provides a reference algorithm for the airborne point clouds processing field that requires high timeliness, such as reconnaissance and Strike Integrated UAV, ground target identification and tracking, etc.