Performance analysis of a compact and low-cost mapping-grade mobile laser scanning system

Julge, Kalev; Vajakas, Toivo; Ellmann, Artu

doi:10.1117/1.jrs.11.044003

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…Future improvements in determining the earthworks accuracy include mounting compact mobile laser scanning systems (Julge, Ellmann, Vajakas, & Kolka, 2016;Julge, Vajakas, & Ellmann, 2017) on unmanned aerial vehicles. Laser scanning data processing is faster than photogrammetry, and laser beams penetrate vegetation.…”

Section: Volume Calculationsmentioning

confidence: 99%

Unmanned Aerial Vehicle Surveying For Monitoring Road Construction Earthworks

Julge

Ellmann

Köök

2019

BJRBE

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Unmanned aerial vehicle photogrammetry is a surveying technique that enables generating point clouds, 3D surface models and orthophoto mosaics. These are based on photos captured with a camera placed on an unmanned aerial vehicle. Within the framework of this research, unmanned aerial vehicle photogrammetry surveys were carried out over a sand and gravel embankment with the aim of assessing the vertical accuracy of the derived surface models. Flight altitudes, ground control points and cameras were varied, and the impact of various factors on the results was monitored. In addition, the traditional real-time-kinematic Global Navigation Satellite System surveys were conducted for verifications. Surface models acquired by different methods were used to calculate volumes and compare the results with requirements set by Estonian Road Administration. It was found that with proper measuring techniques an accuracy of 5.7 cm for the heights were achieved.

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Section: Volume Calculationsmentioning

confidence: 99%

Unmanned Aerial Vehicle Surveying For Monitoring Road Construction Earthworks

Julge

Ellmann

Köök

2019

BJRBE

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“…The essence of this method is the calibration of feature points and feature planes. Julge et al proposed a calibration method based on bound optimization by quadratic approximation to identify the boresight angles of the mobile laser scanning system [18]. Chan et al presented a system calibration method based on multiple features, and it used a plane feature model and a rigorous three-dimensional catenary curve model to augment the calibration [19].…”

Section: Introductionmentioning

confidence: 99%

Accurate Calibration of a Self‐Developed Vehicle‐Borne LiDAR Scanning System

et al. 2021

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To obtain high-precision measurement data using vehicle-borne light detection and ranging (LiDAR) scanning (VLS) systems, calibration is necessary before a data acquisition mission. Thus, a novel calibration method based on a homemade target ball is proposed to estimate the system mounting parameters, which refer to the rotational and translational offsets between the LiDAR sensor and inertial measurement unit (IMU) orientation and position. Firstly, the spherical point cloud is fitted into a sphere to extract the coordinates of the centre, and each scan line on the sphere is fitted into a section of the sphere to calculate the distance ratio from the centre to the nearest two sections, and the attitude and trajectory parameters of the centre are calculated by linear interpolation. Then, the real coordinates of the centre of the sphere are calculated by measuring the coordinates of the reflector directly above the target ball with the total station. Finally, three rotation parameters and three translation parameters are calculated by two least-squares adjustments. Comparisons of the point cloud before and after calibration and the calibrated point clouds with the point cloud obtained by the terrestrial laser scanner show that the accuracy significantly improved after calibration. The experiment indicates that the calibration method based on the homemade target ball can effectively improve the accuracy of the point cloud, which can promote VLS development and applications.

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“…Algorithms and techniques are being also developed by not large companies and scientific institutes. Some of them construct low-cost and compact MLS systems which demand developing additional methods of data adjustment (Julge et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The Study of Mobile Laser Scanning Data Adjustment Results for Large Scale Topographic Mapping

Altyntsev

Saber

2020

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

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Abstract. Mobile laser scanning (MLS) data are widely used for solving various tasks. To be sure that these data are appropriate for a specific task it is necessary to adjust data with a certain accuracy. Large scale topographic mapping is one from the tasks often solved by MLS data. Necessary accuracy of creating topographic plans is determined with a requirements document. Topographic plans are always created in a certain coordinate system. For this reason, MLS data should be previously transformed in the required one. For transformation control points measured with other more accurate methods should be applied. The quantity of necessary control points depends on a surveying area. For urban areas a lot of control points are required due to bad quality of GNSS signal. Much research has been conducted for these areas. For areas with open view of the sky it is required significantly fewer control points. Moreover, there are not so many vertical objects in areas with open view of the sky. Large errors can take place in the result of automatic adjustment of point cloud’s multi-passes. The results of both relative and absolute MLS data adjustment are given for the area with a good GNSS signal. The paper presents the results of accuracy estimation with different quantity of control points. The main goal of the paper is to determine the minimum number of control points for MLS data to be appropriate for creating topographic plans at a scale of 1:500 with a contour interval of 0.25 m.

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Performance analysis of a compact and low-cost mapping-grade mobile laser scanning system

Cited by 13 publications

References 37 publications

Unmanned Aerial Vehicle Surveying For Monitoring Road Construction Earthworks

Unmanned Aerial Vehicle Surveying For Monitoring Road Construction Earthworks

Accurate Calibration of a Self‐Developed Vehicle‐Borne LiDAR Scanning System

The Study of Mobile Laser Scanning Data Adjustment Results for Large Scale Topographic Mapping

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