## 3 Bias Impact Analysis
### Calibration Strategy for UAV-Based LiDAR System  
In this section, we propose a strategy to calibrate the mounting parameters of the LiDAR unit with respect to the onboard GNSS/INS unit using geometric tie features (e.g., planar and linear/cylindrical features). The conceptual basis for LiDAR system calibration is to minimize the discrepancies among conjugate linear and/or planar features obtained from different flight lines. Owing to the irregular distribution of LiDAR points, conjugate point pairs cannot be used since there is no accurate point-to-point correspondence. So, conjugate linear/cylindrical and planar features, such as building facades, ground patches, light poles and lane markers, are used and these can be directly extracted from overlapping areas within the flight lines. After collecting data from several flight lines, a 3D point cloud relative to a global reference frame will be derived using the GNSS/INS unit position and orientation and initial estimates for the mounting parameters. Then, conjugate features are identified and extracted from the reconstructed point cloud. Finally, an iterative LiDAR system calibration with weight modification [18] is proposed to derive the mounting parameters based on the minimization of normal distance between conjugate features. However, conjugate feature extraction from several flight lines could be time-consuming and inefficient, especially when the initial estimates for mounting parameters used to reconstruct the 3D point cloud are considerably inaccurate. To facilitate automated identification of conjugate features in such cases, specifically designed calibration boards covered by highly reflective surfaces, that could be easily deployed and set up in outdoor environments, are used in this study. More specifically, various traffic signs (75 cm wide Stop signs, 90 cm \(\times\) 60 cm Wrong Way signs and 60 cm \(\times\) 60 cm checkerboard targets) are used as highly reflective boards. The highly reflective boards can be easily identified from intensity data, as shown in Figure 8, where the points belonging to these boards exhibit higher intensity values compared to other LiDAR points. First, a pre-defined threshold is set to extract high-intensity points. To avoid the extraction of high-intensity points belonging to objects other than these boards, an approximate pre-set region is set as seed points for each board. Then, a distance-based region growing technique is adopted to group the high intensity board returns. Finally, a plane-fitting is done for these points and the points lying within a normal distance threshold from the best-fitting plane are extracted. Other planar features, such as ground patches or wall patches, can be extracted by defining two diagonally opposite corners and selecting the points lying within a buffer bounding box. The bounding box is constructed around the planar feature of interest by adding a buffer value (in \(X\), \(Y\) and \(Z\) directions) to the coordinates of diagonally opposite corners. Again, a plane-fitting is done for the points contained inside the box and the ones lying within a normal distance threshold from the best-fitting plane are extracted. Linear/cylindrical features can also be used for calibration and they are extracted by specifying the two end points for each feature. A buffer radius is set to define a cylinder around the linear feature of interest. Then, a line-fitting is done for the points lying within this cylindrical buffer and finally, the points that lie within a normal distance threshold from the best-fitting line are extracted. Note that the buffer values for linear/planar feature extraction are determined based on the accuracy of initial estimates of the mounting parameters. The mounting parameters that are derived from calibration are the lever arm (\(\Delta X\), \(\Delta Y\)) and boresight angles (\(\Delta\omega\), \(\Delta\phi\), \(\Delta\kappa\)) for the LiDAR unit.