Charuco Board-Based Omnidirectional Camera Calibration Method

An, Gwon Hwan; Lee, Siyeong; Seo, Min-Woo; Yun, Kugjin; Cheong, Won‐Sik; Kang, Suk‐Ju

doi:10.3390/electronics7120421

Cited by 59 publications

(29 citation statements)

References 36 publications

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“…To register different views reliably and conveniently, the ChArUco board (24) was adopted, as shown in Fig. 4(a).…”

Section: Registration Of Multiview Informationmentioning

confidence: 99%

Manipulated-Object Detection and Pose Estimation Based on Multimodal Feature Points and Neighboring Patches

Tong¹,

Li²,

Zhao³

et al. 2020

Sensors and Materials

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The detection and pose estimation of objects in human demonstrations remain challenging yet crucial tasks. The increasing availability of red-green-blue and depth sensors makes it possible to synthetize local features of color and three-dimensional (3D) geometry, which are useful for processing a wider range of objects. However, existing methods fail to combine the inherent advantages of these two features. Moreover, pose refinement methods based on whole point clouds are often affected by occlusion and background noise. In this paper, feature points of the speeded-up robust feature and the fast point feature histogram were transformed into the same 3D space. After matching them separately, multimodal feature points were jointly used to estimate a coarse pose. Subsequently, the coarse pose was refined by aligning point clouds composed of feature points' neighboring patches. During the iterative closest point process, we selected corresponding points in matched local patches. In our first and second comparative experiments, F1 scores were respectively increased by 0.1349 and 0.1633, which verified the validity of our method. Finally, the third qualitative experiment showed that the proposed method is applicable to manipulated-object detection and pose estimation.

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“…To register different views reliably and conveniently, the ChArUco board (24) was adopted, as shown in Fig. 4(a).…”

Section: Registration Of Multiview Informationmentioning

confidence: 99%

Manipulated-Object Detection and Pose Estimation Based on Multimodal Feature Points and Neighboring Patches

Tong¹,

Li²,

Zhao³

et al. 2020

Sensors and Materials

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show abstract

“…The first aspect of employing an optical-based MMS relates to the problem of multi-camera calibration [2][3][4][5][6][7][8][9][10][11]. Nowadays, many MMSs are equipped with multi-projective cameras (MPC) because of their sturdy design, large field of view (FOV), and promising sensor models.…”

Section: Introductionmentioning

confidence: 99%

“…A synchronous shutter mechanism is applied to take simultaneous shots (<1 msec delay). A geometric model for MPC integrated into a statistical adjustment model is proposed by many researchers, e.g., ( [9][10][11]). This model ensures desirable geometric accuracies for many tasks such as 3D mapping and surveying, 3D visualization, and texturing.…”

Section: Introductionmentioning

confidence: 99%

Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System

et al. 2019

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Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor synchronization, accuracy of georeferencing and stability of geometric configuration of space intersections. In this study, we focus on multi-camera calibration (interior and relative orientation parameter estimation) and MMS calibration (mounting parameter estimation). The objective of this study was to develop a practical scheme for rigorous and accurate system calibration of a photogrammetric mapping station equipped with a multi-projective camera (MPC) and a global navigation satellite system (GNSS) and inertial measurement unit (IMU) for direct georeferencing. The proposed technique is comprised of two steps. Firstly, interior orientation parameters of each individual camera in an MPC and the relative orientation parameters of each cameras of the MPC with respect to the first camera are estimated. In the second step the offset and misalignment between MPC and GNSS/IMU are estimated. The global accuracy of the proposed method was assessed using independent check points. A correspondence map for a panorama is introduced that provides metric information. Our results highlight that the proposed calibration scheme reaches centimeter-level global accuracy for 3D point positioning. This level of global accuracy demonstrates the feasibility of the proposed technique and has the potential to fit accurate mapping purposes.

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“…Several similar methods for calculating the position and orientation of a camera in space using a single image have been described and presented [22,25,26]. Nevertheless, pose estimation and marker detection are widely used tasks for many other technological applications such as autonomous robots [27][28][29], unmanned vehicles [30][31][32][33][34][35][36][37], and virtual assistants [38][39][40][41], among others.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Positioning Algorithm Using a Single Image in an LCD-Camera System by Mesh Elements’ Recalculation and Angle Error Orientation

Ortiz¹,

Amestoy

Reinoso

et al. 2019

Materials

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In this article, we present a method to position the tool in a micromachine system based on a camera-LCD screen positioning system that also provides information about angular deviations of the tool axis during its running. Both position and angular deviations are obtained by reducing a matrix of LEDs in the image to a single rectangle in the conical perspective that is treated by a photogrammetry method. This method computes the coordinates and orientation of the camera with respect to the fixed screen coordinate system. The used image consists of 5 × 5 lit LEDs, which are analyzed by the algorithm to determine a rectangle with known dimensions. The coordinates of the vertices of the rectangle in space are obtained by an inverse perspective computation from the image. The method presents a good approximation of the central point of the rectangle and provides the inclination of the workpiece with respect to the LCD screen reference system of coordinates. A test of the method is designed with the assistance of a Coordinate Measurement Machine (CMM) to check the accuracy of the positioning method. The performed test delivers a good accuracy in the position measurement of the designed method. A high dispersion in the angular deviation is detected, although the orientation of the inclination is appropriate in almost every case. This is due to the small values of the angles that makes the trigonometric function approximations very erratic. This method is a good starting point for the compensation of angular deviation in vision based micromachine tools, which is the principal source of errors in these operations and represents the main volume in the cost of machine elements’ parts.

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Charuco Board-Based Omnidirectional Camera Calibration Method

Cited by 59 publications

References 36 publications

Manipulated-Object Detection and Pose Estimation Based on Multimodal Feature Points and Neighboring Patches

Manipulated-Object Detection and Pose Estimation Based on Multimodal Feature Points and Neighboring Patches

Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System

Enhanced Positioning Algorithm Using a Single Image in an LCD-Camera System by Mesh Elements’ Recalculation and Angle Error Orientation

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