Parameter Recovery for LIDAR Data Calibration Using Natural Surfaces

This paper focuses on recovering systematic biases during LIDAR calibration, particularly using natural surfaces as control features. Many previous approaches have utilized all the points overlapping with the control features and often experienced with an inaccurate value converged with a poor rate due to wrong correspondence in pairing a point and the corresponding control features. To overcome these shortcomings, we establish a preventive scheme to select the pairs of high confidence, where the confidence value is based on the error budget associated with the point measurement and the linearity and roughness of the control feature. This approach was then applied to calibraring the LIDAR data simulated with the given systematic biases. The parameters were successfully recovered using the proposed approach with the accuracy and convergence rate superior to those using the previous approaches.