Alternative Methodologies for the Internal Quality Control of Parallel LiDAR Strips

Habib, Ayman; Kersting, Ana Paula; Bang, Ki-In; Lee, Dong-Cheon

doi:10.1109/tgrs.2009.2026424

“…is used to denote post-mission procedures for evaluating the quality of the final Lidar data product (Habib et al, 2010). The user of the data is more concerned with the final product quality, than the system level Quality Assurance procedures that may vary depending on the type of instrument in use.…”

Section: Quality Control (Qc)mentioning

confidence: 99%

“…The user wants to avoid situations as shown in Figure 1, without having to understand the entire data acquisition process and sensor models. (Habib et al 2010;Latypov 2002;Sande 2010) have discussed methods of reporting registration errors between adjacent strips of Lidar data. The registration errors can be treated as indicators of the quality of calibration.…”

Section: Quality Control (Qc)mentioning

confidence: 99%

Geometric Quality Assessment of Lidar Data Based on Swath Overlap

Sampath

¹

,

Heidemann

²

,

Stensaas

³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

0

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ABSTRACT:This paper provides guidelines on quantifying the relative horizontal and vertical errors observed between conjugate features in the overlapping regions of lidar data. The quantification of these errors is important because their presence quantifies the geometric quality of the data. A data set can be said to have good geometric quality if measurements of identical features, regardless of their position or orientation, yield identical results. Good geometric quality indicates that the data are produced using sensor models that are working as they are mathematically designed, and data acquisition processes are not introducing any unforeseen distortion in the data. High geometric quality also leads to high geolocation accuracy of the data when the data acquisition process includes coupling the sensor with geopositioning systems. Current specifications (e.g. Heidemann 2014) do not provide adequate means to quantitatively measure these errors, even though they are required to be reported. Current accuracy measurement and reporting practices followed in the industry and as recommended by data specification documents also potentially underestimate the interswath errors, including the presence of systematic errors in lidar data.

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“…As in Latypov, 2002, we are more concerned with providing a method of quantifying the relative accuracy of point cloud by making measurements between the points in the overlapping regions. In this paper, we do not talk about "correcting" the data either by adjustment of calibration parameters (Habib et al, 2010) or by the practice of strip adjustment (e.g., Munjy 2015). Latypov analyses the vertical differences between conjugate surface patches in the overlapping regions of the point cloud as a function of surface density and flatness.…”

Section: Lidar Data Geometric Assessmentmentioning

confidence: 99%

Geometric Quality Assessment of Lidar Data Based on Swath Overlap

Sampath¹,

Heidemann²,

Stensaas³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

0

View full text Add to dashboard Cite

ABSTRACT:This paper provides guidelines on quantifying the relative horizontal and vertical errors observed between conjugate features in the overlapping regions of lidar data. The quantification of these errors is important because their presence quantifies the geometric quality of the data. A data set can be said to have good geometric quality if measurements of identical features, regardless of their position or orientation, yield identical results. Good geometric quality indicates that the data are produced using sensor models that are working as they are mathematically designed, and data acquisition processes are not introducing any unforeseen distortion in the data. High geometric quality also leads to high geolocation accuracy of the data when the data acquisition process includes coupling the sensor with geopositioning systems. Current specifications (e.g. Heidemann 2014) do not provide adequate means to quantitatively measure these errors, even though they are required to be reported. Current accuracy measurement and reporting practices followed in the industry and as recommended by data specification documents also potentially underestimate the interswath errors, including the presence of systematic errors in lidar data.

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“…There are two main groups of calibration techniques: (1) system driven calibration, based on physical models, requires raw measurements such as distances, scanning angles, position and attitude for each pulse; (2) data driven compensation, when the resulting data (point clouds) is refined with strip adjustment (Habib et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A Light-Weight Laser Scanner for Uav Applications

Tommaselli¹,

Torres²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Unmanned Aerial Vehicles (UAV) have been recognized as a tool for geospatial data acquisition due to their flexibility and favourable cost benefit ratio. The practical use of laser scanning devices on-board UAVs is also developing with new experimental and commercial systems. This paper describes a light-weight laser scanning system composed of an IbeoLux scanner, an Inertial Navigation System Span-IGM-S1, from Novatel, a Raspberry PI portable computer, which records data from both systems and an octopter UAV. The performance of this light-weight system was assessed both for accuracy and with respect to point density, using Ground Control Points (GCP) as reference. Two flights were performed with the UAV octopter carrying the equipment. In the first trial, the flight height was 100 m with six strips over a parking area. The second trial was carried out over an urban park with some buildings and artificial targets serving as reference Ground Control Points. In this experiment a flight height of 70 m was chosen to improve target response. Accuracy was assessed based on control points the coordinates of which were measured in the field. Results showed that vertical accuracy with this prototype is around 30 cm, which is acceptable for forest applications but this accuracy can be improved using further refinements in direct georeferencing and in the system calibration.

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Alternative Methodologies for the Internal Quality Control of Parallel LiDAR Strips

Cited by 57 publications

References 18 publications

Geometric Quality Assessment of Lidar Data Based on Swath Overlap

Geometric Quality Assessment of Lidar Data Based on Swath Overlap

Geometric Quality Assessment of Lidar Data Based on Swath Overlap

A Light-Weight Laser Scanner for Uav Applications

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