@inproceedings{d2fe67dc3c824eaabf352453307c6cef,
title = "Comparative evaluation of 3D pose estimation of industrial objects in RGB pointclouds",
abstract = "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.",
keywords = "Feature descriptor, Performance evaluation, Pointcloud registration, Pose estimation, Robot vision",
author = "Bjarne Gro{\ss}mann and Mennatullah Siam and Volker Kr{\"u}ger",
year = "2015",
month = jun,
day = "19",
doi = "10.1007/978-3-319-20904-3_30",
language = "English",
isbn = "978-3-319-20903-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer VS",
pages = "329--342",
editor = "Lazaros Nalpantidis and Kr{\"u}ger, { Volker} and Eklundh, { Jan-Olof} and Antonios Gasteratos",
booktitle = "Computer Vision Systems",
note = "10th International Conference on Computer Vision Systems, ICVS 2015 ; Conference date: 06-07-2015 Through 09-07-2015",
}