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

Yin, Hesheng; Ma, Zhe; Ming, Zhong; Wu, Kuan Wei; Wei, Yuteng; Guo, Jun; Huang, Bo

doi:10.3390/s20030621

Cited by 19 publications

(14 citation statements)

References 47 publications

(59 reference statements)

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“…It is very important for the vision system to complete camera calibration quickly. Stability of calibration results: The stability of the calibration results of the proposed algorithm is compared with those of Literature [ 7 ], Literature [ 8 ], Literature [ 9 ], Literature [ 11 ] , and Literature [ 12 ].The measurement of stability is based on the change of camera calibration result data sequence. It is assumed that the calibration data series has the same keywords.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…The method of camera calibration has been proposed by a number of academics both at home and abroad. Literature [ 7 ] proposed an alternative adjustment-based camera calibration algorithm for binocular stereo vision systems, established a binocular vision calibration system with left and right camera coordinates as reference coordinates, and optimized the internal parameters of the two cameras through alternating adjustment experiments to achieve the best value. The optimal distortion parameters and internal and external parameters are then obtained by optimizing all internal and external parameters although the algorithm's convergence time is slow.…”

Section: Related Workmentioning

confidence: 99%

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High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning

Ren

Guan

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Camera calibration is the most important aspect of computer vision research. To address the issue of insufficient precision, therefore, a high precision calibration algorithm for binocular stereo vision camera using deep reinforcement learning is proposed. Firstly, a binocular stereo camera model is established. Camera calibration is mainly divided into internal and external parameter calibration. Secondly, the internal parameter calibration is completed by solving the antihidden point of the camera light center and the camera distortion value of the camera plane. The deep learning fitting value function is used based on the internal parameters. The target network is established to adjust the parameters of the value function, and the convergence of the value function is calculated to optimize reinforcement learning. The deep reinforcement learning fitting structure is built, the camera data is entered, and the external parameter calibration is finished by continuous updating and convergence. Finally, the high precision calibration of the binocular stereo vision camera is completed. The results show that the calibration error of the proposed algorithm under different sizes of checkerboard calibration board test is only 0.36% and 0.35%, respectively, the calibration accuracy is high, the value function converges quickly, and the parameter calculation accuracy is high, the overall time consumption of the proposed algorithm is short, and the calibration results have strong stability.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning

Ren

Guan

Wang

et al. 2022

Computational Intelligence and Neuroscience

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“…Taking advantages of SLAM in visual localization, a user can estimate camera relative pose by several corresponding points. For instance, Yin et al [21] constructed 3D feature point map of the natural environment. The extrinsic matrix is obtained through the 3D scene point map created by SLAM.…”

Section: Related Workmentioning

confidence: 99%

UAV-Assisted Wide Area Multi-Camera Space Alignment Based on Spatiotemporal Feature Map

Jing

Xie

et al. 2021

Remote Sensing

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In this paper, we investigate the problem of aligning multiple deployed camera into one united coordinate system for cross-camera information sharing and intercommunication. However, the difficulty is greatly increased when faced with large-scale scene under chaotic camera deployment. To address this problem, we propose a UAV-assisted wide area multi-camera space alignment approach based on spatiotemporal feature map. It employs the great global perception of Unmanned Aerial Vehicles (UAVs) to meet the challenge from wide-range environment. Concretely, we first present a novel spatiotemporal feature map construction approach to represent the input aerial and ground monitoring data. In this way, the motion consistency across view is well mined to overcome the great perspective gap between the UAV and ground cameras. To obtain the corresponding relationship between their pixels, we propose a cross-view spatiotemporal matching strategy. Through solving relative relationship with the above air-to-ground point correspondences, all ground cameras can be aligned into one surveillance space. The proposed approach was evaluated in both simulation and real environments qualitatively and quantitatively. Extensive experimental results demonstrate that our system can successfully align all ground cameras with very small pixel error. Additionally, the comparisons with other works on different test situations also verify its superior performance.

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“…It is an important technology in 3D shape measurement. Using the known spatial mutual position relationship, two or more cameras obtain the scene image of the same measured object at the same time through the theory of polar geometry and parallax principle to obtain the 3D geometric information of the measured object, so as to achieve the accurate measurement of the object [5]. Zhang [6] introduced BSVS into the field of health monitoring of Chinese ancient buildings for the overall deformation monitoring process of Chinese ancient buildings.…”

Section: Introductionmentioning

confidence: 99%

The Application of Visual Sensors in the Artistic Design of College Campus Landscape

Kan

Cao

2021

Journal of Sensors

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The research purpose is to solve the problems of low efficiency, low accuracy, and high cost of traditional environmental landscape mapping and landscape volume measurement methods in the artistic design of college campus landscape and make up the defects that the traditional campus monitoring is vulnerable to adverse weather, which results in low monitoring accuracy. Primarily, a binocular stereo vision measurement based on Scale Invariant Feature Transform (SIFT) matching algorithm is proposed, which can realize accurate collection of environmental spatial information and measurement of landscape volume without contact in the process of campus landscape design. Then, the visual monitoring system of college landscape based on the Internet of Things (IoT) is constructed to realize real-time monitoring and early warning of human damage to campus landscape. The proposed method is verified by actual measurement of different objects and simulation experiments using simulation software. Ultimately, the application of visual sensors in artistic design of college campus landscape is analysed by literature analysis. The results show that (1) the error of the improved binocular stereo vision measurement designed here is 52.32% and 59.69% lower than that of the traditional measurement method when measuring the same object with different volumes and the volumes of different objects, respectively, which indicates that the measurement accuracy of the new method is higher. (2) The proposed landscape visual monitoring method based on IoT improves the image recognition accuracy by 21% compared with the traditional digital image monitoring method. The average recognition time is shortened by 12 ms, which ensures the accuracy and improves the recognition efficiency. (3) Through the analysis of existing literature, it is found that the binocular stereo vision sensor can be used to monitor the whole process of landscape construction in real time. The sensor can be combined with social networks, mobile terminals, and physiological monitoring equipment to comprehensively analyse and evaluate people’s preference for campus landscape. The proposed method has broad application prospects in campus landscape design, construction, and maintenance. The research purpose is to provide important technical support for the improvement of the overall image of the college campus and even the city for the design of landscape environment and the technical upgrading of maintenance work in the college campus.

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

Cited by 19 publications

References 47 publications

High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning

High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning

UAV-Assisted Wide Area Multi-Camera Space Alignment Based on Spatiotemporal Feature Map

The Application of Visual Sensors in the Artistic Design of College Campus Landscape

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