The understanding of the effects of error on Mobile Terrestrial LiDAR (MTL) point clouds has not increased with their popularity. In this study, comprehensive error analyses based on error propagation theory and global sensitivity study were carried out to quantitatively describe the effects of various error sources in a MTL system on the point cloud. Two scenarios were envisioned; the first using the uncertainties for measurement and calibration variables that are normally expected for MTL systems as they exist today, and the second using an ideal situation where measurement and calibration values have been well adjusted. It was found that the highest proportion of error in the point cloud can be attributed to the boresight and lever arm parameters for MTL systems calibrated using non-rigours methods. In particular, under a loosely controlled error condition, the LiDAR to INS Z lever arm and the LiDAR to INS roll angle contributed more error in the output point cloud than any other parameter, including the INS position. Under tightly controlled error conditions, the INS position became the dominant source of error in the point cloud. In addition, conditional variance analysis has shown that the majority of the error in a point cloud can be attributed to the individual variables. Errors caused by the interactions between the diverse variables are minimal and can be regarded as insignificant.
Regulatory Light Chain Mutants Linked to Heart Disease Modify the Cardiac Myosin Lever Arm
