Chen He

Boresight Calibration of Airborne LiDAR System Without Ground Control Points

This letter proposes a new method for boresight misalignment calibration of the charge-coupled device (CCD) camera which is one component of an airborne light detection and ranging (LiDAR) system without ground control points (GCPs). In the calibration, tie points in overlapping areas are first selected, and then, a multibaseline forward intersection is used for calculating object coordinates of these points. In the intersection, exterior elements of the CCD camera are obtained directly from positioning and orientation system (POS) data of the LiDAR system, which are error contaminated mainly due to the unparallel relation between the frameworks of the inertial measurement unit of the POS and the CCD camera. Elevation values of the ground points are then refined by those obtained from LiDAR point clouds by interpolation, which can be considered to be more accurate than those obtained by multibaseline forward intersection. Through projecting the ground points with refined elevation values into the image space by collinear equations and minimizing distances between the image points selected manually and those projected from ground points, the boresight misalignment is removed effectively. Therefore, the proposed method without GCPs in the whole process is more flexible than other traditional photogrammetric ways.

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.