Calibrating multiple cameras with non-overlapping views using coded checkerboard targets

Strauß, Tobias; Ziegler, Julius; Beck, Johannes

doi:10.1109/itsc.2014.6958110

Cited by 38 publications

(19 citation statements)

References 8 publications

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“…A calibration target is a workpiece with high precision and can be used for feature extraction and precision verification in camera calibration [14,15,[19][20][21]23,[25][26][27][28]32,81]. Planar calibration target is the most widely used calibration target, whose plane consistency is good, and features are easy to extract.…”

Section: Methods Based On Large-scale Targetsmentioning

confidence: 99%

Global calibration of non-overlapping cameras: State of the art

Xia

Zhao

et al. 2018

Optik

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Section: Methods Based On Large-scale Targetsmentioning

confidence: 99%

Global calibration of non-overlapping cameras: State of the art

Xia

Zhao

et al. 2018

Optik

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“…The dataset comprises data recorded from four Velodyne VLP16 LiDARs mounted flat on the roof, three BlackFly PGE-50S5M cameras behind the front-and rear windshield, and a Ublox C94-M8P GNSS receiver. All sensors are jointly calibrated using approaches proposed in [12], [13]. The data contains four passes mapped and geo-referenced using approaches proposed in [4], [14].…”

Section: Experimental Evaluation a Datasetmentioning

confidence: 99%

Localization in Aerial Imagery with Grid Maps using LocGAN

Zhu

Wirges

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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In this work, we present LocGAN, our localization approach based on a geo-referenced aerial imagery and LiDAR grid maps. Currently, most self-localization approaches relate the current sensor observations to a map generated from previously acquired data. Unfortunately, this data is not always available and the generated maps are usually sensor setup specific. Global Navigation Satellite Systems (GNSS) can overcome this problem. However, they are not always reliable especially in urban areas due to multi-path and shadowing effects. Since aerial imagery is usually available, we can use it as prior information. To match aerial images with grid maps, we use conditional Generative Adversarial Networks (cGANs) which transform aerial images to the grid map domain. The transformation between the predicted and measured grid map is estimated using a localization network (LocNet). Given the geo-referenced aerial image transformation the vehicle pose can be estimated. Evaluations performed on the data recorded in region Karlsruhe, Germany show that our LocGAN approach provides reliable global localization results.

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“…Additionally, methods for calibrating both intrinsic and extrinsic parameters were proposed [31][32][33][34][35]. Li et al [31] proposed a method of creating a random pattern by reverse-engineered scale-invariant feature transform [36], which detects feature points that are highly invariant to various distortions.…”

Section: Single Camera Calibrationmentioning

confidence: 99%

“…Li et al [31] proposed a method of creating a random pattern by reverse-engineered scale-invariant feature transform [36], which detects feature points that are highly invariant to various distortions. Strauß et al [32] proposed a method combining the advantages of existing intrinsic and extrinsic calibration methods using a coded checkerboard. Yu et al [33] proposed a camera calibration using a virtual large planar target for a camera with a large field of view (FoV).…”

Section: Single Camera Calibrationmentioning

confidence: 99%

Charuco Board-Based Omnidirectional Camera Calibration Method

Lee

Seo

et al. 2018

Electronics

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In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

show abstract

Calibrating multiple cameras with non-overlapping views using coded checkerboard targets

Cited by 38 publications

References 8 publications

Global calibration of non-overlapping cameras: State of the art

Global calibration of non-overlapping cameras: State of the art

Localization in Aerial Imagery with Grid Maps using LocGAN

Charuco Board-Based Omnidirectional Camera Calibration Method

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