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.
Highlights
Light Detection and Ranging (LiDAR)-based data acquisition is gaining widespread recognition as an efficient and cost-effective technique for rapid collection of 3D geospatial data
The modified weight is determined according to the direction of the planar/linear feature as obtained from the feature parameters derived by a plane/line fitting conducted on the points from the flight line that captures the most number of points belonging to the corresponding feature
Having conducted a thorough analysis of the impact of biases in the different mounting parameters of a Unmanned Aerial Vehicle (UAV)-based LiDAR system to devise an optimal flight and target configuration and proposed a calibration strategy, we conduct several experiments to validate the feasibility of the proposed strategy and quality of calibration, followed by an evaluation of the devised optimal configuration based on the standard deviation and correlation matrix for the mounting parameters for various test cases
Summary
LiDAR-based data acquisition is gaining widespread recognition as an efficient and cost-effective technique for rapid collection of 3D geospatial data. 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.
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