## 5 Conclusions and Recommendations for Future Research  
In this paper, we conducted a thorough bias impact analysis for UAV-based LiDAR systems consisting of a spinning multi-beam laser scanner. Based on this analysis, an optimal target primitive setup and flight line configuration was devised for calibrating a UAV LiDAR system. Finally, an iterative calibration strategy is proposed for deriving the system parameters using different types of conjugate features (i.e., planar, linear/cylindrical) at the same time. The analysis is validated using three different experimental setups which indicate that the most accurate and reliable results are obtained when the established optimal configuration of target primitives and flight lines is used. The UAV  
\begin{table}
\begin{tabular}{c|c|c c|c c|c c} \hline \hline
**Calibration** & **Before** & \multicolumn{2}{c|}{**After Calibration:**} & \multicolumn{2}{c|}{**After Calibration:**} & \multicolumn{2}{c}{**After Calibration:**} \\
**Results** & **Calibration** & \multicolumn{2}{c|}{**Case 1**} & \multicolumn{2}{c|}{**Case 2**} & \multicolumn{2}{c}{**Case 3**} \\ \hline \(H\) **(m)** & & \multicolumn{2}{c|}{**15 m**} & \multicolumn{2}{c|}{**15 m**} & \multicolumn{2}{c}{**15 m \& \multicolumn{2}{c}{**25 m**} \\ \hline \(\delta_{o}\) **(m)** & & \multicolumn{2}{c|}{**0.0312**} & \multicolumn{2}{c|}{**0.0222**} & \multicolumn{2}{c}{**0.0224**} \\ \hline
**Feature ID** & **RMSE (m)** & \multicolumn{2}{c|}{**No. of**} & \multicolumn{2}{c|}{**RMSE (m)**} & \multicolumn{2}{c|}{**No. of**} & \multicolumn{2}{c}{**RMSE (m)**} \\ \hline  & \multicolumn{4}{c|}{**Reflective Boards**} & & \\ \hline Board 0 & 0.0813 & 3006 & 0.0202 & 3006 & 0.0232 & 4568 & 0.0252 \\ Board 1 & 0.0676 & 2987 & 0.0215 & 2987 & 0.0198 & 4596 & 0.0217 \\ Board 2 & 0.0928 & 4683 & 0.0188 & 4683 & 0.0221 & 7397 & 0.0244 \\ Board 3 & 0.0762 & 4735 & 0.0198 & 4735 & 0.0189 & 7462 & 0.0211 \\ Board 4 & 0.0923 & 4851 & 0.0169 & 4851 & 0.0205 & 7578 & 0.0229 \\ Board 5 & 0.0791 & 4456 & 0.0196 & 4456 & 0.0193 & 7062 & 0.0205 \\ Board 6 & 0.0794 & 2736 & 0.0167 & 2736 & 0.0199 & 4320 & 0.0208 \\ Board 7 & 0.0712 & 2736 & 0.0196 & 2736 & 0.0189 & 4361 & 0.0204 \\ Board 8 & 0.0842 & 4566 & 0.0190 & 4566 & 0.0221 & 7140 & 0.0231 \\ Board 9 & 0.0759 & 4492 & 0.0187 & 4492 & 0.0183 & 7045 & 0.0194 \\ Board 10 & 0.0872 & 4934 & 0.0186 & 4934 & 0.0217 & 7829 & 0.0230 \\ Board 11 & 0.0740 & 4976 & 0.0206 & 4976 & 0.0198 & 8031 & 0.0210 \\ Board 12 & 0.0791 & 2996 & 0.0170 & 2996 & 0.0193 & 4628 & 0.0213 \\ Board 13 & 0.0694 & 3198 & 0.0199 & 3198 & 0.0193 & 4999 & 0.0208 \\ Board 14 & 0.0823 & 4420 & 0.0170 & 4420 & 0.0196 & 6562 & 0.0228 \\ Board 15 & 0.0763 & 4907 & 0.0173 & 4907 & 0.0175 & 7304 & 0.0197 \\ \hline  & \multicolumn{4}{c}{**Hut Surfaces**} & & \\ \hline Surface 0 & 0.1097 & 3359 & 0.0734 & 3359 & 0.0736 & 5292 & 0.0895 \\ Surface 1 & 0.1019 & 3613 & 0.0708 & 3613 & 0.0720 & 5610 & 0.0880 \\ Surface 2 & 0.1488 & 3879 & 0.0146 & 3879 & 0.0173 & 6181 & 0.0259 \\ Surface 3 & 0.1305 & 457 LiDAR system calibration attained an accuracy of about 2 cm, which is better than the expected accuracy of around 5-6 cm, keeping in mind the accuracies of the hardware involved. This indicates that the proposed calibration strategy is efficient and accurate. Future work will focus on combining the mounting parameters (i.e., extrinsic parameters) and sensor parameters (i.e., intrinsic parameters) to obtain a comprehensive calibration leading to even more accurate point clouds. Furthermore, although the proposed model is capable of incorporating horizontal ground patches to estimate the lever-arm \(\Delta Z\) component, no ground control points had been observed for the experimental datasets due to which these results could not be addressed in this work. This ability would be demonstrated in our future works aimed at simultaneous calibration of mounting parameters and intrinsic sensor parameters.