Abstract
Abstract. Outliers and noise in point cloud data are unavoidable due to intrinsic and/or extrinsic survey factors. Significant errors may result from false geometry produced by a collection of anomalies, compounded by sparse structure, irregular densities, and lack of geometric cohesion typical of point clouds nature. Thus, filtering techniques on raw data are required to produce accurate point clouds suitable for further processing. This objective is pursued in the following study through a comparative analysis between two registered clouds, one obtained from TLS, used as reference dataset and the other – to be filtered – from SLAM system. Four steps make up the workflow: analysing the comparison models’ geometric attributes, specifically surface density and roughness; constructing statistical tolerance limits for the TLS cloud’s roughness distribution; cleaning the SLAM cloud; assessing the filtering outcomes. Our efforts to effectively remove and mitigate noise, while preserving the original detail features of the object surface, have been driven by the detailed articulation of point cloud denoising approaches that have been introduced in recent years. However, in this wide context, our goal is not to provide a review or to explore the details of the various methods; rather, we want to offer a simple yet efficient method for obtaining an integrated model with a uniform noise level. This can be especially useful when the data from the survey will later be used in source-based modelling.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.