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

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. 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 s...

Fully Automated Profile-based Calibration Strategy for Airborne and Terrestrial Mobile LiDAR Systems with Spinning Multi-beam Laser Units

“…There are two major components associated with the development of any calibration strategy: (1) definition of an optimal set of calibration primitives and drive-run/flight line configuration, and (2) development of a point-pairing strategy along with an optimization function for calibration. Ravi et al [32,33] conducted a theoretical bias impact analysis for terrestrial and airborne MLMS to suggest an optimal track and calibration primitive configuration. Their study recommended to include calibration primitives that provide variability in three-dimensional coordinates of constituent points with respect to the tracks capturing these primitives to ensure sufficient control for an accurate calibration.…”

“…These targets are not required for the proposed profile-based calibration study. However, they are deployed in the field in order to be used to conduct the manually-assisted feature-based calibration strategy proposed by Ravi et al [33], the results from which were to validate the accuracy 600,000 points per second 300,000 points per second Apart from the system components for the two airborne MLMS, another difference lies in the relative orientation in which the LiDAR unit is mounted with respect to the GNSS/IMU body frame for the two systems. The nominal boresight angles relating the LiDAR unit and GNSS/IMU body frame in airborne MLMS 1 are 90 • , 90 • , and 0 • for the roll, pitch, and heading angles, respectively, and those for airborne MLMS 2 are 90 • , −90 • , and 0 • , respectively.…”

“…One should note that these nominal values indicate that both systems will have a gimbal lock during boresight calibration due to a secondary rotation of ±90 • . As suggested by Ravi et al [33], such a gimbal lock problem can be mitigated by introducing a virtual laser unit coordinate frame (Lu ) approximately aligned with the IMU body frame and thereafter estimating the boresight angles relating the virtual frame and IMU body frame during calibration. The discussed coordinate systems (original and virtual laser unit coordinate frames and IMU body frame) are shown in Figure 1.…”

“…These targets are not required for the proposed profile-based calibration study. However, they are deployed in the field in order to be used to conduct the manually-assisted feature-based calibration strategy proposed by Ravi et al [33], the results from which were to validate the accuracy of fully automated profile-based calibration strategy. One should note that the test field was similar for both UAV systems, with minor variations in the distance between the deployed targets.…”

“…A previous study by Ravi et al [32,33] proposed a manually assisted feature-based calibration strategy for MLMS and proved its ability to attain the best possible accuracy in keeping with the manufacturer's specifications for the onboard sensors. Therefore, in addition to conducting a profile-based calibration, all the systems were also calibrated using the existing manually assisted feature-based calibration technique.…”