Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Antti Hietanen"'
Autor:
Ville Kyrki, Antti Hietanen, Jyrki Latokartano, Alessandro Foi, Joni-Kristian Kamarainen, Roel Pieters, Minna Lanz
Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825196. Publisher Copyright: © 2021 The Author(s) Robot grasping and manipulation require estima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ed35812afc1ae9dc6a98f3a74701749
Autor:
Roel Pieters, Antti Hietanen, Roni-Jussi Halme, Minna Lanz, Jyrki Latokartano, Joni Kamarainen
Publikováno v:
Procedia CIRP. 72:111-116
Safety in human-robot collaborative manufacturing is ensured by various types of safety systems that help to avoid collisions and limit impact forces to the acceptable level in a case of collisions. Recently, active vision-based safety systems have g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f506a04fcc4ac27fd5da0d70fedcd14f
https://doi.org/10.1016/j.procir.2018.03.043
https://doi.org/10.1016/j.procir.2018.03.043
Autor:
Antti Hietanen, Alireza Changizi, Roel Pieters, Pallab Ganguly, Jyrki Latokartano, Joni Kamarainen, Minna Lanz
Publikováno v:
RO-MAN
In the past years human-robot collaboration has gained interest among industry and production environments. While there is interest towards the topic, there is a lack of industrially relevant cases utilizing novel methods and technologies. The feasib
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bdd19293a7c01812a14ca79d7e381755
https://doi.org/10.1109/ro-man46459.2019.8956446
https://doi.org/10.1109/ro-man46459.2019.8956446
Publikováno v:
Neurocomputing. 184:3-12
Solid protocols to benchmark local feature detectors and descriptors were introduced by Mikolajczyk et al. 1,2. The detectors and the descriptors are popular tools in object class matching, but the wide baseline setting in the benchmarks does not cor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::925e2a1621daba94b2cec6cbc2620a94
https://doi.org/10.1016/j.neucom.2015.08.106
https://doi.org/10.1016/j.neucom.2015.08.106
Publikováno v:
MESA
Recent development in depth sensing provide various opportunities for the development of new methods for Human Robot Interaction (HRI). Collaborative robots (co-bots) are redefining HRI across the manufacturing industry. However, little work has been
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::902db3a88e011e7b412061a976722ca5
https://trepo.tuni.fi/handle/10024/129401
https://trepo.tuni.fi/handle/10024/129401
Publikováno v:
VISIGRAPP (4: VISAPP)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cc2c510feda4814560347de88ba6dc3
https://doi.org/10.5220/0006619202870296
https://doi.org/10.5220/0006619202870296
Publikováno v:
ICRA
Hietanen, A, Halme, J, Buch, A G, Latokartano, J & Kämäräinen, J-K 2017, Robustifying Correspondence Based 6D Object Pose Estimation . in Proceedings of the 2017 IEEE International Conference on Robotics and Automation . IEEE Press, I E E E International Conference on Robotics and Automation, pp. 739-745, IEEE International Conference on Robotics and Automation, Singapore, 29/05/2017 . https://doi.org/10.1109/ICRA.2017.7989091
Hietanen, A, Halme, J, Buch, A G, Latokartano, J & Kämäräinen, J-K 2017, Robustifying Correspondence Based 6D Object Pose Estimation . in Proceedings of the 2017 IEEE International Conference on Robotics and Automation . IEEE Press, I E E E International Conference on Robotics and Automation, pp. 739-745, IEEE International Conference on Robotics and Automation, Singapore, 29/05/2017 . https://doi.org/10.1109/ICRA.2017.7989091
We propose two methods to robustify point correspondence based 6D object pose estimation. The first method, curvature filtering, is based on the assumption that low curvature regions provide false matches, and removing points in these regions improve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::114d239c8b6b3ca8d18e8cd8dc52fa55
https://doi.org/10.1109/icra.2017.7989091
https://doi.org/10.1109/icra.2017.7989091
Autor:
Paproki, Anthony1 (AUTHOR) anthony.paproki@gmail.com, Salvado, Olivier2 (AUTHOR) olivier.salvado@csiro.au, Fookes, Clinton3 (AUTHOR) c.fookes@qut.edu.au
Publikováno v:
ACM Computing Surveys. Nov2024, Vol. 56 Issue 11, p1-37. 37p.
Autor:
Jussi Halme, Alireza Changizi, Antti Hietanen, Jyrki Matias Latokartano, Minna Lanz, Joni Kämäräinen, Koskinen, Kari T.
Publikováno v:
Tampere University
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::03c8a537d0cf3ed0653d534c49d7d6b3
https://researchportal.tuni.fi/en/publications/0242ac94-d31a-4079-817a-d334d1a111d4
https://researchportal.tuni.fi/en/publications/0242ac94-d31a-4079-817a-d334d1a111d4
Autor:
Antti Hietanen, Jussi Halme, Jyrki Matias Latokartano, Roel Pieters, Minna Lanz, Joni Kämäräinen
Publikováno v:
Tampere University
acceptedVersion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::00a0d6eed849dab18283e828681c99b5
https://researchportal.tuni.fi/en/publications/a0304948-f855-42dc-b059-a57d98f2acc4
https://researchportal.tuni.fi/en/publications/a0304948-f855-42dc-b059-a57d98f2acc4