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