A novel plane estimation algorithm from 3D range data is presented. The proposed solution is based on the minimization of a nonlinear prediction error cost function inspired by the mathematical definition of Gibbs' entropy. The method has been experimentally tested and compared with a standard implementation of the RANSAC algorithm. Results suggest that the proposed approach has the potential of performing better in terms of precision and reliability while requiring a lower computational effort.

Robust 3D Plane Estimation for Autonomous Vehicle Applications

DISTANTE, Cosimo;INDIVERI, GIOVANNI
2010-01-01

Abstract

A novel plane estimation algorithm from 3D range data is presented. The proposed solution is based on the minimization of a nonlinear prediction error cost function inspired by the mathematical definition of Gibbs' entropy. The method has been experimentally tested and compared with a standard implementation of the RANSAC algorithm. Results suggest that the proposed approach has the potential of performing better in terms of precision and reliability while requiring a lower computational effort.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/349075
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