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Research Articles

An automatic calibration algorithm for endoscopic structured light sensors in cylindrical environment

, , , & ORCID Icon
Pages 513-535 | Received 22 Nov 2022, Accepted 04 May 2023, Published online: 25 May 2023
 

ABSTRACT

Structured light sensing systems, as one of the most common optical-based nondestructive evaluation techniques, have been widely applied for inline pipeline inspection. The sensor can be inserted inside the pipe to generate 3D visualisation and evaluate the cracks in the materials. The precise calibration of the camera-projector measurement system is of great significance to ensure the measurement accuracy of the 3D sensing system. Conventional calibration methods for structured light sensors involve complicated and time-consuming procedures and are easily affected by ambient light. The paper presents a novel algorithm to automatically calibrate the projection module and estimate the stereo parameters between the camera and the projector. The calibration algorithm exploits the cylindrical nature of the inspected pipe to create a set of geometric constraints and automatically calibrate the sensor without the need for reference calibration points. Experimental and simulation results showed that the algorithm could successfully estimate the projector’s intrinsic and extrinsic parameters by simply acquiring the data inside a cylindrical pipe with a known diameter. The proposed algorithm highly reduces the data collection time for the calibration (only 53 s), improves the accuracy, and simplifies the calibration process.

Acknowledgments

This work was supported by the US Department of Transportation/Pipeline and Hazardous Materials Safety Administration (DOT/PHMSA) through the AI-enabled ILI robot with integrated structured light NDE for distribution pipelines under Award 693JK31850007CAAP.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the US Department of Transportation/Pipeline [693JK31850007CAAP].

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