An Interval Branch and Bound Algorithm for Parameter Estimation and Application to Stereovision - École des Ponts ParisTech Access content directly
Conference Papers Year : 2019

An Interval Branch and Bound Algorithm for Parameter Estimation and Application to Stereovision

Abstract

The parameter estimation problem is a challenging problem in engineering sciences consisting in computing the parameters of a parametric model that fit observed data. The system is defined by unknown parameters and sometimes internal constraints. The observed data provide constraints on the parameters. The parameter estimation problem is particularly difficult when some observation constraints correspond to outliers and/or the constraints are non convex. For dealing with outliers, the RANSAC ran-domized algorithm is efficient, but non deterministic, and must be specialized for every problem. In this work, we propose a generic interval branch and bound algorithm that produces a model maximizing the number of observation constraints satisfied within a given tolerance. This tool is inspired by the IbexOpt Branch and Bound algorithm for constrained global optimization (NLP) and is endowed with an improved version of a relaxed intersection operator applied to the observations. The latest version of our B&B follows the Feasible diving strategy to visit the nodes in the search tree. Experiments on a stereovision problem have validated the approach.
Fichier principal
Vignette du fichier
gow18_bbestim_final.pdf (2.76 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01927682 , version 1 (20-11-2018)

Identifiers

Cite

Bertrand Neveu, Martin de La Gorce, Pascal Monasse, Gilles Trombettoni. An Interval Branch and Bound Algorithm for Parameter Estimation and Application to Stereovision. LeGO 2018 - 14th International Workshop on Global Optimization, Sep 2018, Leiden, Netherlands. ⟨10.1063/1.5089999⟩. ⟨hal-01927682⟩
207 View
148 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More