Adjustment of Discrepancies Between LIDAR Data Strips Using Linear Features

Lee, Jae Bin; Yu, Kiyun; Kim, Yong‐Il; Habib, Ayman

doi:10.1109/lgrs.2007.898079

Cited by 50 publications

(46 citation statements)

References 4 publications

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“…Based on surfaces from photogrammetric procedures, Bretar et al [37] proposed a methodology for the adjustment problem of the airborne laser scanner strip, in which aerial images over the same area are needed. Lee et al [38] employed lines to measure and adjust the discrepancies between overlapping LiDAR data strips, in which TINs are created for the segmentation of points and line equations are determined from the intersection of neighboring planar patches. Habib et al [39] presented three alternative methodologies for the internal quality control of parallel LiDAR strips using pseudo-conjugate points, conjugate areal and linear features and point-to-patch correspondence, respectively.…”

Section: Registration Of Airborne Lidar Stripsmentioning

confidence: 99%

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

Cheng

Tong

et al. 2015

Remote Sensing

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Abstract:A new automated approach to the high-accuracy registration of airborne and terrestrial LiDAR data is proposed, which has three primary steps. Firstly, airborne and terrestrial LiDAR data are used to extract building corners, known as airborne corners and terrestrial corners, respectively. Secondly, an initial matching relationship between the terrestrial corners and airborne corners is automatically derived using a matching technique based on maximum matching corner pairs with minimum errors (MTMM). Finally, a set of leading points are generated from matched airborne corners, and a shiftable leading point method is proposed. The key feature of this approach is the implementation of the concept of shiftable leading points in the final step. Since the geometric accuracy of terrestrial LiDAR data is much better than that of airborne LiDAR data, leading points corresponding to anomalous airborne corners could be modified for the improvement of the geometric accuracy of registration. OPEN ACCESSRemote Sens. 2015, 7 1916The experiment demonstrates that the proposed approach can advance the geometric accuracy of two-platform LiDAR data registration effectively.

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Section: Registration Of Airborne Lidar Stripsmentioning

confidence: 99%

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

Cheng

Tong

et al. 2015

Remote Sensing

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“…Of particular concern is the matching of 3D line features, which utilizes angle and distance constraints. Lee et al [29] employed lines to measure and adjust the discrepancies between overlapping data strips, in which triangulated irregular networks are created for the segmentation of points and line equations are determined from the intersection of neighboring planar patches. More recently, after the global registration of point clouds, Bucksch et al extracted skeletons from tree branches and exploited the matching of these skeletons [30].…”

Section: Review Of Registration Of Point Clouds From the Same Platformmentioning

confidence: 99%

Semi-Automatic Registration of Airborne and Terrestrial Laser Scanning Data Using Building Corner Matching with Boundaries as Reliability Check

Cheng

Tong

et al. 2013

Remote Sensing

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Data registration is a prerequisite for the integration of multi-platform laser scanning in various applications. A new approach is proposed for the semi-automatic registration of airborne and terrestrial laser scanning data with buildings without eaves. Firstly, an automatic calculation procedure for thresholds in density of projected points (DoPP) method is introduced to extract boundary segments from terrestrial laser scanning data. A new algorithm, using a self-extending procedure, is developed to recover the extracted boundary segments, which then intersect to form the corners of buildings. The building corners extracted from airborne and terrestrial laser scanning are reliably matched through an automatic iterative process in which boundaries from two datasets are compared for the reliability check. The experimental results illustrate that the proposed approach provides both high reliability and high geometric accuracy (average error of 0.44 m/0.15 m in horizontal/vertical direction for corresponding building corners) for the final registration of airborne laser scanning (ALS) and tripod mounted terrestrial laser scanning (TLS) data.

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“…(1) in which the boresight angles are left as unknowns and solved based on control points, control features or common features between strips (Habib et al, 2010;Lee et al, 2007;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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Adjustment of Discrepancies Between LIDAR Data Strips Using Linear Features

Cited by 50 publications

References 4 publications

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

Semi-Automatic Registration of Airborne and Terrestrial Laser Scanning Data Using Building Corner Matching with Boundaries as Reliability Check

A Light-Weight Laser Scanner for Uav Applications

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