Airborne LiDAR strip adjustment based on conjugate linear features

An airborne LiDAR is a complex multi-sensor integrated system. The existence of systematic errors will lead to discrepancies between overlapping strips. This paper presents a algorithm to detect and adjust such discrepancies and creat a seamless dataset. Due to the irregular nature of the LiDAR data, Linear features are used and a point-to-point correspondence are built by extracting the endpoints of conjugate linear features. Firstly, linear features are extracted from the point clouds in overlapping strips. Secondly, endpoints of these linear features are obtained and tie points matching are also accomplished. Further, an improved Bursa model is used to adjust overlapping strips through a least squares matching procedure. At last, an experiment with real datasets is carried out to verify that the methodology is effective and efficient. The root mean square error (RMSE) between conjugate points is used to evaluate the accuracy after adjustment.