Effective lens distortion correction for 3D range scannig systems

Chmelar, Pavel; Beran, Ladislav; Rejfek, Lubos; Kudriavtseva, Nataliia V.

doi:10.1109/elmar.2015.7334490

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The lower focal length caused the undesired barrel distortion effect. This effect elimination is described in reference [37]. In one of our recent papers ( [38]), we developed the automatic algorithm for the best undistortion parameter estimation.…”

Section: Optical Rangefinder For Space Scanningmentioning

confidence: 99%

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

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Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method. This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. This proposed approach is verified on the real indoor point clouds.

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Section: Optical Rangefinder For Space Scanningmentioning

confidence: 99%

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

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“…In one of our recent papers [13] we are dealing with simple algorithm for barrel distortion correction. The used camera lens has wide angle range especially for increasing the vertical FOV, which increases the number of measurement points along the axis.…”

Section: Introductionmentioning

confidence: 99%

The Automatic Undistortion Strength Estimation for Any Describable Optical Distortion

Chmelarova

Chmelar

Rejfek

2019

2019 29th International Conference Radioelektronika (RADIOELEKTRONIKA)

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A presence of an optical distortion does undesired influence on the geometrical image quality. The main reason of its occurring is using a lens with low or variable focal length or simple camera optics. When an image captured by a camera is affected by undesired distortion and it is used for the distance measurement, it can cause significant measurement errors. The aim of this paper is to introduce a method for finding an optimal undistortion parameter for any optical distortion, which can be described mathematically. The proposed method uses the Hough transformation accumulator and at each iteration step the quality of straight lines is evaluated as a gradient from local maximum. The algorithm is verified and assessed on several images. The developed approach can be used to find the global maximum gradient in general, even when a local maximum changes its size over iterations.

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“…In our previous work we introduced the new modelling of a colored laser line by using the laser Gaussian Mixture Model (GMM) [7]. The last paper describes an elimination of the barrel distortion correction [8]. These works contribute to the increase of the measurement points amount and measurement precision.…”

Section: Introductionmentioning

confidence: 99%

The Depth Map Construction from a 3D Point Cloud

2016

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Abstract.A depth map transforms 3D points into a 2D image and gives a different view of an observed scene. This paper deals with a depth map construction. It describes the whole process, how to transform any 3D point cloud into a 2D depth map. The described method uses 3D rotation matrixes and the line equation. This process allows to create the desired view from arbitrary point and rotation in an exploration space. Using of a depth map allows to apply image processing methods on depth data to get additional information about an ambient space.

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Effective lens distortion correction for 3D range scannig systems

Cited by 4 publications

References 8 publications

Advanced Methods for Point Cloud Processing and Simplification

Advanced Methods for Point Cloud Processing and Simplification

The Automatic Undistortion Strength Estimation for Any Describable Optical Distortion

The Depth Map Construction from a 3D Point Cloud

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