A Clustering and Outlier Detection Scheme for Robust Parametric Model Estimation for Plane Fitting

Abstract:

Extraction of geometric information and reconstruction of a parametric model from the data points captured by various sensors or generated by various image preprocessing algorithms is a vital research issue for many computer vision and robotics applications. The aim is to reconstruct 3D objects, consisting of planar patches, in a scene from its point cloud captured by a sensor set. A reconstructed scene has many applications such as stereo vision, robot navigation, medical imaging, etc. Unfortunately, the captured point cloud often gets corrupted due to sensor errors/malfunctioning and preprocessing algorithms. The corrupted data pose difficulty in accurate estimation of underlying geometric model parameters. In this paper, a new algorithm has been proposed to efficiently and accurately estimate the model parameters in heavily corrupted data points. The method is based on forming clusters of estimated planes with reference to a fixed plane. Clustering is accomplished on the basis of angles and distances of estimated planes from the reference plane. The proposed method is implemented over a wide range of data points. It is a robust technique and observed to outperform the widely used RANSAC algorithm in terms of accuracy and computational efficiency.

• Periodical: Applied Mechanics and Materials (Volumes 789-790)
• Pages: 770-775
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.789-790.770
• Online since: September 2015
• Authors: Rachna Verma*, Arvind Kumar Verma
• Keywords: Clustering, Inliers/Outliers, Plane-Fitting, RANSAC, Robust Model

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