RANSAC-based Point Cloud Calibration Algorithm Accounting for Point Quantity and Distribution

Abstract

To tackle the challenge of inadequate accuracy in UAV-LiDAR point cloud data, this research introduces a RANSAC calibration algorithm that takes into account both the quantity and spatial distribution of points. Simulation experiments were carried out across four experimental sites to ascertain the optimal point placement strategy under diverse terrain conditions. Initially, control points were established within the experimental zones, and both control point data and point cloud data were collected. Subsequently, calibration strategies incorporating different point counts and distributions were devised. Ultimately, the precision of each calibration approach was evaluated. The experimental findings reveal that: (1) calibration accuracy decreases as terrain complexity increases within the experimental areas; (2) a greater number of points correlates with higher calibration accuracy; (3) given an equal number of points, a diamond-shaped point distribution yields the highest calibration accuracy.