###### Abstract  
Light Detection and Ranging (LiDAR) is a technology that uses laser beams to measure ranges and generates precise 3D information about the scanned area. It is rapidly gaining popularity due to its contribution to a variety of applications such as Digital Building Model (DBM) generation, telecommunications, infrastructure monitoring, transportation corridor asset management and crash/accident scene reconstruction. To derive point clouds with high positional accuracy, estimation of mounting parameters relating the laser scanners to the onboard Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) unit, i.e., the lever-arm and boresight angles, is the foremost and necessary step. This paper proposes a LiDAR system calibration strategy for a Unmanned Aerial Vehicle (UAV)-based mobile mapping system that can directly estimate the mounting parameters for spinning multi-beam laser scanners through an outdoor calibration procedure. This approach is based on the use of conjugate planar/linear features in overlapping point clouds derived from different flight lines. Designing an optimal configuration for calibration is the first and foremost step in order to ensure the most accurate estimates of mounting parameters. This is achieved by conducting a rigorous theoretical analysis of the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud. The dependency of the impact on the orientation of target primitives and relative flight line configuration would help in deducing the configuration that would maximize as well as decouple the impact of bias in each mounting parameter so as to ensure their accurate estimation. Finally, the proposed analysis and calibration strategy are validated by calibrating a UAV-based LiDAR system using two different datasets--one acquired with flight lines at a single flying height and the other with flight lines at two different flying heights. The calibration performance is evaluated by analyzing correlation between the estimated system parameters, 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. LiDAR; UAV; laser scanners; 3D point clouds; GNSS/INS; calibration; bias impact analysis; Least Squares Adjustment 2018 297 201  
the increasing range of applications--such as digital building model generation, transportation corridor monitoring, telecommunications, precision agriculture, infrastructure monitoring, seamless outdoor-indoor mapping, power line clearance evaluation and crash scene reconstruction [1; 2; 3; 4; 5]--have led to the development of multi-unit mobile LiDAR systems onboard airborne and terrestrial platforms that are either manned or unmanned. However, the attainment of the full positioning potential of such systems is contingent on an accurate calibration of the mobile mapping unit as a whole, wherein we need to estimate the mounting parameters relating the onboard units and the internal characteristics of the sensor. Some of the applications mentioned above can be effectively met using UAV platforms [6; 7; 8; 5]. The sensors onboard a UAV platform need to be commensurate with the payload restrictions as well as their effectiveness for the desired mapping and monitoring application. The cost-effective Velodyne laser scanner (a spinning multi-beam laser unit) can rapidly capture a high volume of data and has been used in many mobile mapping systems and robotics applications [9; 10; 11]. Over the past few years, a great deal of research has been devoted to modeling the inherent systematic errors in Velodyne laser scanners as well as the calibration of LiDAR systems to estimate the internal sensor characteristics and mounting parameters [12; 13]. [PERSON] et al. (2016) [14] performed a geometric calibration with stationary VLP-16 to marginally improve the accuracy of the point clouds by approximately 20%.