Notes on a Robust Plane Detection Approach in 3D

This paper addresses the issue of plane detection in 3 dimensional (3D) range images. The identification of planar structures is a crucial task in many visual-aided autonomous robotic applications. The proposed method consists in implementing, in cascade, two algorithms: Random Sample and Consensus (RANSAC) and the more recent Least Entropy-like Estimator (LEL), a nonlinear prediction error estimator that minimizes a cost function inspired by the definition of Gibbs entropy. LEL estimators allow to improve RANSAC performances while maintaining its robustness; kernel density estimation is used to classify data into inliers and outliers. The method has been experimentally applied to 3D images acquired by a Time-Of- Flight camera and compared with a stand alone RANSAC solution. The proposed solution does not require an accurate estimation of the noise variance or outlier scale. This is of fundamental practical importance as the outlier scale, while severely influencing standard RANSAC, is usually unknown a priori and hard to estimate.