Simultaneous Recognition and Relative Pose Estimation of 3D Objects Using 4D Orthonormal Moments

Domínguez Cabrerizo, Sergio (2017). Simultaneous Recognition and Relative Pose Estimation of 3D Objects Using 4D Orthonormal Moments. "Sensors", v. 17 (n. 9); pp. 1-22. ISSN 1424-8220. https://doi.org/10.3390/s17092122.

Description

Title: Simultaneous Recognition and Relative Pose Estimation of 3D Objects Using 4D Orthonormal Moments
Author/s:
  • Domínguez Cabrerizo, Sergio
Item Type: Article
Título de Revista/Publicación: Sensors
Date: September 2017
ISSN: 1424-8220
Volume: 17
Subjects:
Freetext Keywords: 3D object recognition; relative pose estimation; orthonormal moments
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (4MB) | Preview

Abstract

Both three-dimensional (3D) object recognition and pose estimation are open topics in the research community. These tasks are required for a wide range of applications, sometimes separately, sometimes concurrently. Many different algorithms have been presented in the literature to solve these problems separately, and some to solve them jointly. In this paper, an algorithm to solve them simultaneously is introduced. It is based on the definition of a four-dimensional (4D) tensor that gathers and organizes the projections of a 3D object from different points of view. This 4D tensor is then represented by a set of 4D orthonormal moments. Once these moments are arranged in a matrix that can be computed off-line, recognition and pose estimation is reduced to the solution of a linear least squares problem, involving that matrix and the 2D moments of the observed projection of an unknown object. The abilities of this method for 3D object recognition and pose estimation is analytically proved, demonstrating that it does not rely on experimental work to apply a generic technique to these problems. An additional strength of the algorithm is that the required projection is textureless and defined at a very low resolution. This method is computationally simple and shows very good performance in both tasks, allowing its use in applications where real-time constraints have to be fulfilled. Three different kinds of experiments have been conducted in order to perform a thorough validation of the proposed approach: recognition and pose estimation under z axis (yaw) rotations, the same estimation but with the addition of y axis rotations (pitch), and estimation of the pose of objects in real images downloaded from the Internet. In all these cases, results are encouraging at a similar level to those of state-of-the art algorithms

More information

Item ID: 50151
DC Identifier: http://oa.upm.es/50151/
OAI Identifier: oai:oa.upm.es:50151
DOI: 10.3390/s17092122
Official URL: https://www.mdpi.com/1424-8220/17/9/2122
Deposited by: Memoria Investigacion
Deposited on: 01 Jun 2018 15:58
Last Modified: 30 May 2019 08:27
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM