Assessing Lidar Accuracy with Hexagonal Retro-Reflective Targets

Canavosio-Zuzelski, Roberto; Hogarty, James; Rodarmel, Craig; Lee, Mark; Braun, Aaron

doi:10.14358/pers.79.7.663

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…However, planar features may be absent in non-urban settings. Other methods introduce artificial control points [12] and reflective targets [13] in the scene. Others rely on matching points [14], profiles [15] and features [16] in two overlapping LiDAR scans.…”

Section: Ins Rotationmentioning

confidence: 99%

Globally Optimal Boresight Alignment of UAV-LiDAR Systems

Gopinath¹,

Hijazi²,

Collins³

et al. 2022

Preprint

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In airborne light detection and ranging (LiDAR) systems, misalignments between the LiDAR-scanner and the inertial navigation system (INS) mounted on an unmanned aerial vehicle (UAV)'s frame can lead to inaccurate 3D point clouds. Determining the orientation-offset, or boresight error, is key to many LiDAR-based applications. In this work, we introduce a mixed-integer quadratically constrained quadratic program (MIQCQP) that can globally solve this misalignment problem. We also propose a nested spatial branch and bound (nsBB) algorithm that improves computational performance. The nsBB relies on novel preprocessing steps that progressively reduce the problem size. In addition, an adaptive grid search (aGS) allowing us to obtain quick heuristic solutions is presented. Our algorithms are open-source, multi-threaded and multimachine compatible.

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Section: Ins Rotationmentioning

confidence: 99%

Globally Optimal Boresight Alignment of UAV-LiDAR Systems

Gopinath¹,

Hijazi²,

Collins³

et al. 2022

Preprint

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“…The study in [12] used white L-shaped targets painted on asphalt to similarly locate 3D positions within lidar-derived intensity images using least-squares matching [13]. The study in [14] used retro-reflective targets in a hexagonal configuration to find a location within a relatively sparse point cloud (approximately 2 pts/m 2 ) to aid in boresight calibration, with reported location precision of 5 cm and 4 cm in the vertical and horizontal components, respectively. The dimensions of a proposed, but not yet implemented, foldable 3D target were illustrated by [15], designed to use solely the point positions, and not intensity.…”

Section: Introductionmentioning

confidence: 99%

Geometric Targets for UAS Lidar

et al. 2019

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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.

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“…Cheng et al [10] proposed an approach for registering airborne and terrestrial laser scanning data at building corners. Canavosio-Zuzelski et al [11] used a raised hexagonal retro-reflective lidar ground target (HRRT) to address a point location uncertainty and other researches utilized the geometric targets from airborne or mobile lidar [12,13].…”

Section: Introductionmentioning

confidence: 99%

General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

et al. 2019

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The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (three dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of the external uncertainty model were demonstrated using various lidar point cloud data from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane modeling.

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Assessing Lidar Accuracy with Hexagonal Retro-Reflective Targets

Cited by 8 publications

References 6 publications

Globally Optimal Boresight Alignment of UAV-LiDAR Systems

Globally Optimal Boresight Alignment of UAV-LiDAR Systems

Geometric Targets for UAS Lidar

General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

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