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Bias Impact Analysis and Calibration of UAV-Based Mobile LiDAR System with Spinning Multi-Beam Laser Scanner

2018 Applied Sciences  
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
more » ... n 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. 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 [1-5]-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 [5] [6] [7] [8] . 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] . Glennie et al. (2016) [14] performed a geometric calibration with stationary VLP-16 to marginally improve the accuracy of the point clouds by approximately 20%. 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. Habib 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 proposed
doi:10.3390/app8020297 fatcat:kp35tp5ldjdhhhkvn3ajwphb4i