Abstract ▼ AI-Helper

LIDAH data often include systematic errors, which should be removed by a calibration process. This paper proposes a robust approach to calibrating LIDAR data using natural surfaces as reference data. The uniqueness of this approach is to employ a sophisticated selection scheme so that only a portion...

LIDAH data often include systematic errors, which should be removed by a calibration process. This paper proposes a robust approach to calibrating LIDAR data using natural surfaces as reference data. The uniqueness of this approach is to employ a sophisticated selection scheme so that only a portion of LIDAR points can be used to estimate the bias parameters generating the systematic errors. This approach was applied to calibrating simulated LIDAR data. The results show that the approach can successfully recover the bias parameters and calibrate the data with acceptable RMS errors. Particularly, the parameter recovery model can be easily extended to register image data with LIDAR data.