Rotating optical geometry sensor for inner pipe-surface reconstruction

Moritz Ritter; Christan W. Frey

doi:10.1117/12.838851

Abstract

The inspection of sewer or fresh water pipes is usually carried out by a remotely controlled inspection vehicle equipped with a high resolution camera and a lightning system. This operator-oriented approach based on offline analysis of the recorded images is highly subjective and prone to errors. Beside the subjective classification of pipe defects through the operator standard closed circuit television (CCTV) technology is not suitable for detecting geometrical deformations resulting from e.g. structural mechanical weakness of the pipe, corrosion of e.g. cast-iron material or sedimentations. At Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB) in Karlsruhe, Germany, a new Rotating Optical Geometry Sensor (ROGS) for pipe inspection has been developed which is capable of measuring the inner pipe geometry very precisely over the whole pipe length. This paper describes the developed ROGS system and the online adaption strategy for choosing the optimal system parameters. These parameters are the rotation and traveling speed dependent from the pipe diameter. Furthermore, a practicable calibration methodology is presented which guarantees an identification of the several internal sensor parameters. ROGS has been integrated in two different systems: A rod based system for small fresh water pipes and a standard inspection vehicle based system for large sewer Pipes. These systems have been successfully applied to different pipe systems. With this measurement method the geometric information can be used efficiently for an objective repeatable quality evaluation. Results and experiences in the area of fresh water pipe inspection will be presented.

Citation Download Citation

Moritz Ritter, Moritz Ritter, Christan W. Frey, Christan W. Frey, } "Rotating optical geometry sensor for inner pipe-surface reconstruction", Proc. SPIE 7538, Image Processing: Machine Vision Applications III, 753803 (28 January 2010); doi: 10.1117/12.838851; https://doi.org/10.1117/12.838851

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