Terrestrial Laser Scanner Autonomous Self-Calibration With No Prior Knowledge of Point-Clouds

Abstract

To improve the positional accuracy of point-clouds, a self-calibration model of a terrestrial laser scanner (TLS) was used to calibrate mounting angle errors as systematic errors. The model was based on the TLS scanning mechanism, and parameters of the model were determined by using measured TLS point-clouds. To automatically solve parameters of the self-calibration model, a background target-based autonomous method was proposed to find the feature points used as input for the model. The autonomous self-calibration method presented here involved three steps: determination of initial feature points using a keypoint quality algorithm with no prior knowledge, filtering the initial feature points to find coarse feature points using a $K$ -means algorithm, optimization of coarse feature points to find fine feature points based on measurement precision. In comparison with the auxiliary target-based method, experimental results showed that the background target-based autonomous method is a valid self-calibration method for TLS, with comparable measurement precision and a simplified self-calibration procedure.