Laser Scanners used for Structural Health Monitoring applications such as Pipelines Structural Inspections normally needs Point Clouds from a large quantity of individual measurements that should be adjusted or post-processed to decrease overall point-cloud errors depending on scanner's characteristics. The posterior adjustment is commonly addressed by different mathematical methods or computational algorithms. According to application requirements methods such as machine learning, signal filtering, or RANSAC algorithms are used. This paper shows the application of an adapted/modify RANSAC algorithm especially suited for the pipeline inspection task. Aiming to increase the percentage of useful data per capture.