A novel camera calibration technique based on differential evolution particle swarm optimization algorithm

Deng, Li; Lu, Gen; Shao, Yang; Fei, Minrui; Hu, Huosheng

doi:10.1016/j.neucom.2015.03.119

Cited by 55 publications

(19 citation statements)

References 26 publications

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“…Self-calibration methods: Camera parameters can be obtained under the condition that the scene is unknown from only the constraint relationships obtained in an image sequence. For instance, Deng et al [38] proposed a calibration method based on differential evolution and particle swarm optimization (DEPSO). As shown in Table 1 [38], the results of 10, 80 and 800 groups showed that, compared with other algorithms, the proposed algorithm had the smallest average value (Ave) and standard deviation (Std), so the proposed calibration method performed best.…”

Section: B Overview Of Camera Calibrationmentioning

confidence: 99%

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Zhai

Zhang

et al. 2020

IEEE Access

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Rescue work after a coal mine accident is fraught with challenges and dangers. Considering the safety of rescue workers and the urgency of a rescue mission, it is necessary to use coal mine rescue robots to perform the tasks of environmental detection and rescue. As a key part of the robot sensing system, a visual sensor can provide much information about a scene. Among vision sensor types, binocular vision has the advantages of being noncontact and passive, and it is the key technology for a robot to acquire obstacle information and reconstruct a three-dimensional scene. Therefore, coal mine rescue robots based on binocular vision have become a popular research topic in the field of mine safety. First, the research status of camera calibration and stereo vision matching for binocular vision is systematically introduced in this paper. Second, the latest research progress on coal mine rescue robots based on binocular vision is reviewed from the perspective of technological applications and development. Finally, the technical challenges and future development trends of binocular vision in coal mine rescue robots are described. INDEX TERMS Coal mine rescue robots, binocular vision, camera calibration, stereo vision matching.

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Section: B Overview Of Camera Calibrationmentioning

confidence: 99%

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Zhai

Zhang

et al. 2020

IEEE Access

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“…The relative transformation between the two monocular cameras should be accurate. In Formula (10), the cost function contains a two-part weighted residual. The first partial residual is the same as Formula (5), corresponding to the reprojection errors of the feature observations for left monocular camera keyframes, while the second part corresponds to those for the right monocular camera frame.…”

Section: Joint Optimizationmentioning

confidence: 99%

“…Typical algorithms of traditional calibration methods include the direct linear transformation algorithm (DLT) [7], the nonlinear optimization algorithm [8], and the Tsai's radial alignment constraint algorithm (RAC) [9]. Deng et al [10] proposed a relational model for camera calibration, which takes into account the camera's geometric parameters and lens distortion effects. The combination of differential evolution and particle swarm optimization algorithm can effectively calibrate camera parameters.…”

Section: Introductionmentioning

confidence: 99%

SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time

Yin

Ming

et al. 2020

Sensors

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The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual off-line algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SLAM)-based self-calibration method designed to achieve real-time, automatic and accurate calibration of the binocular stereo vision (BSV) rig’s extrinsic parameters in a short period without auxiliary equipment and special calibration markers, assuming the intrinsic parameters of the left and right cameras are known in advance. The main contribution of this paper is to use the SLAM algorithm as our main tool for the calibration method. The method mainly consists of two parts: SLAM-based construction of 3D scene point map and extrinsic parameter calibration. In the first part, the SLAM mainly constructs a 3D feature point map of the natural environment, which is used as a calibration area map. To improve the efficiency of calibration, a lightweight, real-time visual SLAM is built. In the second part, extrinsic parameters are calibrated through the 3D scene point map created by the SLAM. Ultimately, field experiments are performed to evaluate the feasibility, repeatability, and efficiency of our self-calibration method. The experimental data shows that the average absolute error of the Euler angles and translation vectors obtained by our method relative to the reference values obtained by Zhang’s calibration method does not exceed 0.5˚ and 2 mm, respectively. The distribution range of the most widely spread parameter in Euler angles is less than 0.2˚ while that in translation vectors does not exceed 2.15 mm. Under the general texture scene and the normal driving speed of the mobile robot, the calibration time can be generally maintained within 10 s. The above results prove that our proposed method is reliable and has practical value.

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“…Deng et al proposed a relationship model for camera calibration in which the geometric parameters and the lens distortion effect of the camera were taken into account to unify the WCS, the camera coordinate system (CCS), and the image coordinate system (ICS). (15) In their study, the proposed algorithm can avoid local optimums and complete the visual identification tasks accurately. Ji et al presented a novel design for an automatic and complete parameter calibration system.…”

Section: Introductionmentioning

confidence: 98%

Development of a Color Object Classification and Measurement System Using Machine Vision

Ngo¹,

Porter²,

Hsu³

2019

Sensors and Materials

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Machine-vision-based reading and sorting devices have been used to measure and classify items. Here, we extend their application to the sorting and assembling of items identified by their geometry and color. In this study, we developed an improved machine vision system that is capable of discerning and categorizing items of distinct geometries and colors and utilizes a computer-controlled robotic system to manipulate and segregate these items. Hence, a machine vision system for an automatic classification process while operating a robotic arm is hereby developed. To obtain positioning information, the proposed system uses cameras that were mounted above the working platform to acquire images. Perspective and quadratic transformations were used to transform the image coordinates of the calibration system to the world coordinates by using a calibration procedure. By these methods, the proposed system can ascertain the two-and three-dimensional coordinates of the objects and automatically perform classification and assembly operations using the data collected from the visual recognition system.

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A novel camera calibration technique based on differential evolution particle swarm optimization algorithm

Cited by 55 publications

References 26 publications

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time

Development of a Color Object Classification and Measurement System Using Machine Vision

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