A Robust and Flexible Geometry calibration Method for Phase Measuring Deflectometry

Fan, Xinggang; Ma, Tianle; Wang, Geng; Liu, Jiaxian; Li, Chao

doi:10.1109/cyber55403.2022.9907653

2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) 2022

DOI: 10.1109/cyber55403.2022.9907653

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A Robust and Flexible Geometry calibration Method for Phase Measuring Deflectometry

Xinggang Fan

Tianle Ma

Geng Wang

et al.

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2024

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P‐4.1: Research on Fusion Calibration Algorithm of Camera and IMU improved by Maximum Likelihood Method

Wang,

2024

Symp Digest of Tech Papers

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Sensor calibration is an important step in SLAM, and lightweight, inexpensive sensors are often used in Augmented Reality applications. The accuracy of sensor calibration directly affects the accuracy and reliability of subsequent image processing, recognition, perception, and positioning. The inertial processing unit is robust to the sudden motion effect, blurring appears in the middle of camera motion, and the overlap area between two frames is too small, so it is difficult to match feature points. The camera itself can solve the problem of IMU drift in the middle of slow motion, and the two are complementary. VI‐SLAM, which integrates vision and IMU, has become a hot research topic at present. In this paper, the INDEMIND calibration method is analyzed and improved, the maximum likelihood method is used to improve the calibration accuracy, and its effectiveness is verified in the experiment.

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P‐4.1: Research on Fusion Calibration Algorithm of Camera and IMU improved by Maximum Likelihood Method

Wang,

2024

Symp Digest of Tech Papers

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show abstract

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A Robust and Flexible Geometry calibration Method for Phase Measuring Deflectometry

Cited by 1 publication

References 0 publications

P‐4.1: Research on Fusion Calibration Algorithm of Camera and IMU improved by Maximum Likelihood Method

P‐4.1: Research on Fusion Calibration Algorithm of Camera and IMU improved by Maximum Likelihood Method

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