The Universal Lidar Error Model

Rodarmel, Craig; Lee, Mark; Gilbert, John D.; Wilkinson, Benjamin; Theiss, Henry J.; Dolloff, John; O’Neill, Christopher

doi:10.14358/pers.81.7.543

Cited by 8 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The covariance matrix of the target template transformation parameters, Σ ∆∆ , may be calculated using Equation (5). The elements of Σ ∆∆ .…”

Section: Methodsmentioning

confidence: 99%

“…As with all geospatial data, it is important to characterize the spatial accuracy of lidar products to inform their appropriate use. Similarly, a measure of accuracy is important for rigorously adjusting lidar data, correcting for systematic errors, and fusing it with other geospatial datasets, such as photography [5]. Accuracy assessment of geospatial data typically involves the use of check-points, independently surveyed targets, or features within the captured scene, to produce statistical measures of accuracy often reported using the root mean square error (RMSE).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Geometric Targets for UAS Lidar

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

“…The covariance matrix of the target template transformation parameters, Σ ∆∆ , may be calculated using Equation (5). The elements of Σ ∆∆ .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geometric Targets for UAS Lidar

et al. 2019

View full text Add to dashboard Cite

show abstract

“…As a result, evaluating the mapping accuracy of a GNSS/INS sensor based on the manufacturer's specifications can be difficult. Several published works have rigorously analyzed the effects of trajectory errors on point cloud coordinate accuracies for both UAS-based (e.g., [14]) and occupied aircraftbased laser scanning systems (e.g., [22][23][24]). Thus, a full error analysis is not repeated here; only the effect of heading precision on the positional precision of a point cloud is discussed in order to provide the theoretical foundation for the experiment of this study.…”

Section: Effects Of Heading Precision On the Positional Precision Of A Uas-lidar Point Cloudmentioning

confidence: 99%

Investigating Practical Impacts of Using Single-Antenna and Dual-Antenna GNSS/INS Sensors in UAS-Lidar Applications

Brazeal

Wilkinson

Benjamin

2021

Sensors

View full text Add to dashboard Cite

Data collected from a moving lidar sensor can produce an accurate digital representation of the physical environment that is scanned, provided the time-dependent positions and orientations of the lidar sensor can be determined. The most widely used approach to determining these positions and orientations is to collect data with a GNSS/INS sensor. The use of dual-antenna GNSS/INS sensors within commercial UAS-lidar systems is uncommon due to the higher cost and more complex installation of the GNSS antennas. This study investigates the impacts of using a single-antenna and dual-antenna GNSS/INS MEMS-based sensor on the positional precision of a UAS-lidar generated point cloud, with an emphasis on the different heading determination techniques employed by each type of GNSS/INS sensor. Specifically, the impacts that sensor velocity and acceleration (single-antenna), and a GNSS compass (dual-antenna) have on heading precision are investigated. Results indicate that at the slower flying speeds often used by UAS (≤5 m/s), a dual-antenna GNSS/INS sensor can improve heading precision by up to a factor of five relative to a single-antenna GNSS/INS sensor, and that a point of diminishing returns for the improvement of heading precision exists at a flying speed of approximately 15 m/s for single-antenna GNSS/INS sensors. Additionally, a simple estimator for the expected heading precision of a single-antenna GNSS/INS sensor based on flying speed is presented. Utilizing UAS-lidar mapping systems with dual-antenna GNSS/INS sensors provides reliable, robust, and higher precision heading estimates, resulting in point clouds with higher accuracy and precision.

show abstract

“…The LiDAR systems used in vegetation monitoring usually exploit near-infrared light to scan vegetation stands, single components (e.g., trees), or even subcomponents (e.g., branches and leaves) [10,11]. In fact, a narrow laser beam can map physical features with very high spatial resolution, e.g., an aircraft can map the target at decimetric resolution [12]. A 3-dimensional (3D) dense cloud of laser pulses resulting from a typical ALS campaign represents a collection of numerous 3D points, each having, at least, geographic position and height information.…”

Section: Introductionmentioning

confidence: 99%

Global Airborne Laser Scanning Data Providers Database (GlobALS)—A New Tool for Monitoring Ecosystems and Biodiversity

et al. 2020

View full text Add to dashboard Cite

Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS.

show abstract

The Universal Lidar Error Model

Cited by 8 publications

References 8 publications

Geometric Targets for UAS Lidar

Geometric Targets for UAS Lidar

Investigating Practical Impacts of Using Single-Antenna and Dual-Antenna GNSS/INS Sensors in UAS-Lidar Applications

Global Airborne Laser Scanning Data Providers Database (GlobALS)—A New Tool for Monitoring Ecosystems and Biodiversity

Contact Info

Product

Resources

About