###### Abstract
Moreover, they also investigated the range accuracy of VLP-16, which is quoted to have a Root Mean Square Error (RMSE) value between 22 and 27 mm in the factory supplied calibration certificate. But, it was observed that some of the laser beams have worse range accuracy than others. Although many LiDAR system calibration procedures have been developed in the past, outdoor calibration of integrated GNSS/INS and multi-unit 3D laser scanners is still an active area of research. The major contribution of this work is to develop an optimal/minimal flight and feature configuration for a reliable estimation of mounting parameters. In this regard, minimal refers to the minimal number of flight lines and feature configuration that are required for reliable calibration. On the other hand, optimal denotes decoupling (removing any significant correlation between) various components of the mounting parameters. This can be achieved through a bias impact analysis that evaluates the effect of biases in the mounting on features with different configurations. Before proceeding with the bias impact analysis, we first introduce the components involved in the UAV-based LiDAR system used in this research and discuss the details regarding the synchronization of the hardware units (LiDAR and GNSS/INS units). Then, we focus on developing a calibration strategy for a UAV-based LiDAR system with a spinning multi-beam laser scanner by conducting an in-depth analysis of the impact of biases in the system parameters on the resultant 3D point cloud. The purpose of system calibration is to simultaneously estimate the mounting parameters relating the different system components by minimizing the discrepancy between conjugate linear and/or planar features in overlapping point clouds derived from different flight lines. In this regard, a detailed bias impact analysis facilitates the design of an optimal configuration of target primitives and flight lines for ensuring accurate calibration results. [PERSON] et al. (2010) [15] discussed the bias impact analysis in detail for airborne linear scanners while describing the simplified and quasi-rigorous approaches for calibration, whereas in this research, the bias impact analysis is conducted for a spinning multi-beam laser scanner starting from the 3D point-positioning equation. The optimal target primitive configuration is devised by studying the impact of biases on planes oriented in different directions and the optimal flight line configuration is determined based on the effect of biases arising from flight lines with different directions and lateral separation on planes with varying orientation. It is worth mentioning that the bias impact analysis conducted in this regard is independent of the calibration procedure as the analysis only depends on the mathematical model for 3D LiDAR point positioning. To the best of the authors' knowledge, none of the previous works have addressed the proposal of an optimal/minimal calibration configuration that is independent of the calibration strategy for airborne mobile mapping systems. Finally, based on the results obtained from the bias impact analysis, a feature-based calibration strategy is applied to show the effectiveness of the proposedoptimal configuration by comparing the results obtained using three different configurations for calibration--two with a sub-optimal target primitive and flight line configuration and another with the optimal configuration for calibration as derived according to the bias impact analysis. The results are quantified by analyzing correlation between the estimated system parameters (lever arm and boresight angles), the a-posteriori variance factor of the Least Squares Adjustment (LSA) procedure and the quality of fit of the adjusted point cloud to planar/linear features before and after the calibration process.