Robust Fitting of Geometric Primitives on LiDAR Data

Toth, Tekla; Hajder, Levente

doi:10.5220/0007572606220629

Cited by 4 publications

(3 citation statements)

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“…In the real-world tests, the GT is obtained by fitting a sphere to the LiDAR data using a novel method (Tth. and Hajder., 2019), that is tailored for LiDAR datapoints.…”

Section: Discussionmentioning

confidence: 99%

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Automatic Estimation of Sphere Centers from Images of Calibrated Cameras

Hajder

Toth

Pusztai

2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Calibration of devices with different modalities is a key problem in robotic vision. Regular spatial objects, such as planes, are frequently used for this task. This paper deals with the automatic detection of ellipses in camera images, as well as to estimate the 3D position of the spheres corresponding to the detected 2D ellipses. We propose two novel methods to (i) detect an ellipse in camera images and (ii) estimate the spatial location of the corresponding sphere if its size is known. The algorithms are tested both quantitatively and qualitatively. They are applied for calibrating the sensor system of autonomous cars equipped with digital cameras, depth sensors and LiDAR devices.

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“…In the real-world tests, the GT is obtained by fitting a sphere to the LiDAR data using a novel method (Tth. and Hajder., 2019), that is tailored for LiDAR datapoints.…”

Section: Discussionmentioning

confidence: 99%

“…Even the most accurate CG tools cannot generate full-realistic images, therefore real testing cannot be neglected. The GT data in these test are obtained by using a novel sphere fitting algorithm, designed for LiDAR data processing (Tth. and Hajder., 2019) ,and by LiDAR-camera calibration.…”

Section: Methodsmentioning

confidence: 99%

Automatic Estimation of Sphere Centers from Images of Calibrated Cameras

Hajder

Toth

Pusztai

2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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“…Only a partial point cloud can be reconstructed of a sphere because the point of contact between the ball and the turntable is not visible to the camera. Since the sphere is only partially visible, a robust sphere-fitting algorithm [98] is used to find the sphere. The average distance of the point cloud to the fit sphere is 0.2 mm with 0.009 mm standard deviation.…”

Section: Precision Of the 3d Scannermentioning

confidence: 99%

Accurate Sensor Calibration for Geometric Computer Vision

Pusztai¹

2019

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Calibration of devices is the process of determining some internal or external parameters of a selected mathematical model for a specific device. This dissertation focuses on the calibration of widely used modalities in computer vision, for example, structured-light scanners, cameras, and LiDAR (Light Detection and Ranging) sensors.Calibration is a crucial step for many applications in computer vision. A priori knowledge of parameters (e.g. the focal length or camera poses) makes the processing of other algorithms faster and more accurate. Some of these parameters can be estimated online, during the usage of the device. However, online algorithms achieve lower accuracy in general and only used when the device is unreachable by human and no manual calibration is available. The proposed methods in this dissertation focus on accuracy rather than automatic or object-free calibration.

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