Chosen Aspects of the Production of the Basic Map Using Uav Imagery

Kędzierski, Michał; Fryśkowska, Anna; Wierzbicki, Damian; Nerc, P.

doi:10.5194/isprsarchives-xli-b1-873-2016

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…The Trimble UX-5 platform registered in real time navigational data in the form of co-ordinate geodetic BLh (B-geodetic width, Lgeodetic length, h-ellipsoid height) and HPR rotation angles (Heading H-, P-Pitch, R-Roll) for the UAV. The Trimble UX-5 writes down navigational parameters of position and orientation of an unmanned aircraft in the form of text file with the expansion *.log" (Kedzierski et al, 2016b). The target accuracy of co-ordinate delimitation of an unmanned aircraft should not cross 10 m, whereas accordingly or angles of the turnover is taking out to 5 0 (Eling et al, 2015).…”

Section: Research Experimentsmentioning

confidence: 99%

The Prediction of Position and Orientation Parameters of Uav for Video Imaging

Wierzbicki

2017

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

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ABSTRACT:The paper presents the results of the prediction for the parameters of the position and orientation of the unmanned aerial vehicle (UAV) equipped with compact digital camera. Issue focus in this paper is to achieve optimal accuracy and reliability of the georeferenced video frames on the basis of data from the navigation sensors mounted on UAV. In experiments two mathematical models were used for the process of the prediction: the polynomial model and the trigonometric model. The forecast values of position and orientation of UAV were compared with readings low cost GPS and INS sensors mounted on the unmanned Trimble UX-5 platform. Research experiment was conducted on the preview of navigation data from 23 measuring epochs. The forecast coordinate values and angles of the turnover and the actual readings of the sensor Trimble UX-5 were compared in this paper. Based on the results of the comparison it was determined that: the best results of co-ordinate comparison of an unmanned aerial vehicle received for the storage with, whereas worst for the coordinate Y on the base of both prediction models, obtained value of standard deviation for the coordinate XYZ from both prediction models does not cross over a admissible criterion 10 m for the term of the exactitudes of the position of a unmanned aircraft. The best results of the comparison of the angles of the turn of a unmanned aircraft received for the angle Pitch, whereas worst for the angles Heading and Roll on the base of both prediction models. Obtained value of standard deviation for the angles of turn HPR from both prediction models does not exceed a admissible exactitude 5⁰ only for the angle Pitch, however crosses over this value for the angles Heading and Roll.

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Section: Research Experimentsmentioning

confidence: 99%

The Prediction of Position and Orientation Parameters of Uav for Video Imaging

Wierzbicki

2017

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

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“…A point cloud is generated from photogrammetric images via image matching in a process called multi-image matching or semi-global matching (SGM). This has been discussed in several publications, for example [17,18,19,20,21], while [22,23] presented the generation of point clouds and the identification of unconventional shapes using video images, and [24,25] proposed the use of oblique images.…”

Section: Introductionmentioning

confidence: 99%

Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis

Fryśkowska

2019

Sensors

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Three-dimensional (3D) mapping of power lines is very important for power line inspection. Many remotely-sensed data products like light detection and ranging (LiDAR) have been already studied for power line surveys. More and more data are being obtained via photogrammetric measurements. This increases the need for the implementation of advanced processing techniques. In recent years, there have been several developments in visualisation techniques using UAV (unmanned aerial vehicle) platform photography. The most modern of such imaging systems have the ability to generate dense point clouds. However, image-based point cloud accuracy is very often various (unstable) and dependent on the radiometric quality of images and the efficiency of image processing algorithms. The main factor influencing the point cloud quality is noise. Such problems usually arise with data obtained via low-cost UAV platforms. Therefore, generated point clouds representing power lines are usually incomplete and noisy. To obtain a complete and accurate 3D model of power lines and towers, it is necessary to develop improved data processing algorithms. The experiment tested the algorithms on power lines with different voltages. This paper presents the wavelet-based method of processing data acquired with a low-cost UAV camera. The proposed, original method involves the application of algorithms for coarse filtration and precise filtering. In addition, a new way of calculating the recommended flight height was proposed. At the end, the accuracy assessment of this two-stage filtration process was examined. For this, point quality indices were proposed. The experimental results show that the proposed algorithm improves the quality of low-cost point clouds. The proposed methods improve the accuracy of determining the parameters of the lines by more than twice. About 10% of noise is reduced by using the wavelet-based approach.

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“…Products of photogrammetric processing are nowadays used across many domains, and are available via commercial and open-source software tools. The quality demands of such products vary with applications, e.g., from millimetric accuracy in industrial metrology [ 1 ] to centimetric or decentimetric accuracies in applications concerning indoor mapping [ 2 ], cultural heritage documentation [ 3 ], mobile mapping [ 4 ] or airborne surveys [ 5 , 6 ]. Typically, as the accuracies grow, so does the on-site labour to prepare an acquisition.…”

Section: Introductionmentioning

confidence: 99%

Second Iteration of Photogrammetric Processing to Refine Image Orientation with Improved Tie-Points †

Giang

Muller

Rupnik

et al. 2018

Sensors

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Photogrammetric processing is available in various software solutions and can easily deliver 3D pointclouds as accurate as 1 pixel. Certain applications, e.g., very accurate shape reconstruction in industrial metrology or change detection for deformation studies in geosciences, require results of enhanced accuracy. The tie-point extraction step is the opening in the photogrammetric processing chain and therefore plays a key role in the quality of the subsequent image orientation, camera calibration and 3D reconstruction. Improving its precision will have an impact on the obtained 3D. In this research work we describe a method which aims at enhancing the accuracy of image orientation by adding a second iteration photogrammetric processing. The result from the classical processing is used as a priori information to guide the extraction of refined tie-points of better photogrammetric quality. Evaluated on indoor and UAV acquisitions, the proposed methodology shows a significant improvement on the obtained 3D point accuracy.

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Chosen Aspects of the Production of the Basic Map Using Uav Imagery

Cited by 10 publications

References 7 publications

The Prediction of Position and Orientation Parameters of Uav for Video Imaging

The Prediction of Position and Orientation Parameters of Uav for Video Imaging

Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis

Second Iteration of Photogrammetric Processing to Refine Image Orientation with Improved Tie-Points †

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