Abstract
Lidar from small unoccupied aerial systems (UAS) is a viable method for collecting geospatial data associated with a wide variety of applications. Point clouds from UAS lidar require a means for accuracy assessment, calibration, and adjustment. In order to carry out these procedures, specific locations within the point cloud must be precisely found. To do this, artificial targets may be used for rural settings, or anywhere there is a lack of identifiable and measurable features in the scene. This paper presents the design of lidar targets for precise location based on geometric structure. The targets and associated mensuration algorithm were tested in two scenarios to investigate their performance under different point densities, and different levels of algorithmic rigor. The results show that the targets can be accurately located within point clouds from typical scanning parameters to <2 cm σ , and that including observation weights in the algorithm based on propagated point position uncertainty leads to more accurate results.
Highlights
Airborne lidar, or laser scanning, from small unoccupied aerial systems (UAS) has gained a reputation as a viable mapping tool for both academic researchers and commercial users within the past decade (e.g., [1,2,3])
This study introduces an alternative to intensity-based artificial targets for UAS lidar
We focus on the methods to achieve the accuracy assessment, which can be used for other similar UAS laser scanning systems
Summary
Laser scanning, from small unoccupied aerial systems (UAS) has gained a reputation as a viable mapping tool for both academic researchers and commercial users within the past decade (e.g., [1,2,3]). Hardware calibration is a critical component of processing lidar data and consists of estimating systematic parameter errors associated with the physical alignment of sensor mechanisms [8] These include the lever arm and boresight parameters: the location and angular orientation, respectively, of the scanner head relative to the navigation reference point of the system, usually the inertial measurement unit (IMU). The effects of errors in physical calibration parameters as well as errors not modeled in calibration, e.g., biases in trajectory due to navigation solution drift, may be absorbed into the geometric transformation parameters Both calibration and strip adjustment algorithms often use the identification of points associated with geometric primitives, such as planes or lines in the scene, to detect discrepancies between overlapping collection units to be used as observations in optimization schemes for resolving the parameters.
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