Abstract
We present the Finite-Window Robust Sequential Estimator for the detection and analysis of corrosion in range images of gas pipelines. This statistically robust, real-time technique estimates the pipeline surface range function in the presence of noise, surface deviations, and changes in the underlying model. Deviations from the robust surface fit, corresponding to statistical outliers, represent potential areas of corrosion. Because the algorithm estimates surface parameters over a finite, sliding window of data, it can track moderately high-order surfaces using lower order models. The system is consistent, objective, and non-destructive and can be used with the pipeline in service.
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