Comparative evaluation of 3D pose estimation of industrial objects in RGB pointclouds

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


3D pose estimation is a crucial element for enabling robots to work in industrial environment to perform tasks like bin-picking or depalletizing. Even though there exist various pose estimation algorithms, they usually deal with common daily objects applied in lab environments. However, coping with real-world industrial objects is a much harder challenge for most pose estimation techniques due to the difficult material and structural properties of those objects. A comparative evaluation of pose estimation algorithms in regard to these object characteristics has yet to be done. This paper aims to provide a description and evaluation of selected state-of-the-art pose estimation techniques to investigate their object-related performance in terms of time and accuracy. The evaluation shows that there is indeed not a general algorithm which solves the task for all different objects, but it outlines the issues that real-world application have to deal with and what the strengths and weaknesses of the different pose estimation approaches are.


External organisations
  • Aalborg University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Computer Vision and Robotics (Autonomous Systems)


  • Feature descriptor, Performance evaluation, Pointcloud registration, Pose estimation, Robot vision
Original languageEnglish
Title of host publicationComputer Vision Systems
EditorsLazaros Nalpantidis, Volker Krüger, Jan-Olof Eklundh, Antonios Gasteratos
Number of pages14
ISBN (Electronic)978-3-319-20904-3
ISBN (Print)978-3-319-20903-6
Publication statusPublished - 2015 Jun 19
Publication categoryResearch
Externally publishedYes
Event10th International Conference on Computer Vision Systems, ICVS 2015 - Copenhagen, Denmark
Duration: 2015 Jul 62015 Jul 9

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Computer Vision Systems, ICVS 2015