Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Hermann Blum"'
Autor:
Moritz B. Milde, Hermann Blum, Alexander Dietmüller, Dora Sumislawska, Jörg Conradt, Giacomo Indiveri, Yulia Sandamirskaya
Publikováno v:
Frontiers in Neurorobotics, Vol 11 (2017)
Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement
Externí odkaz:
https://doaj.org/article/45c8a8aa44274e9394f65edcf5e10de8
Publikováno v:
Springer Proceedings in Advanced Robotics ISBN: 9783031255540
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::646d24aad8bb825c4118794048003d52
https://doi.org/10.1007/978-3-031-25555-7_9
https://doi.org/10.1007/978-3-031-25555-7_9
Publikováno v:
2022 International Conference on Robotics and Automation (ICRA).
Autonomous robots deal with unexpected scenarios in real environments. Given input images, various visual perception tasks can be performed, e.g., semantic segmentation, depth estimation and normal estimation. These different tasks provide rich infor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::552aefb4d12cc20793ce81d78e77ae71
https://doi.org/10.1109/icra46639.2022.9812310
https://doi.org/10.1109/icra46639.2022.9812310
This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks fail to generalize well to unseen environments, the agent collects imag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::896048013ddc43b15e8eb5d290a3b891
http://arxiv.org/abs/2203.00549
http://arxiv.org/abs/2203.00549
Publikováno v:
IEEE Robotics and Automation Letters. 5:1032-1038
Deep learning has enabled remarkable advances in scene understanding, particularly in semantic segmentation tasks. Yet, current state of the art approaches are limited to a closed set of classes, and fail when facing novel elements, also known as out
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::278670c63aa30adccaa0d6931bf14c0b
https://doi.org/10.1109/lra.2020.2967313
https://doi.org/10.1109/lra.2020.2967313
Publikováno v:
Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC).
This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82613c062bd56ecb47e7dbd932b1e90a
https://doi.org/10.22260/isarc2021/0102
https://doi.org/10.22260/isarc2021/0102
Publikováno v:
Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5eaf2cee009bd3473597f92766fbd581
https://doi.org/10.22260/isarc2021/0101
https://doi.org/10.22260/isarc2021/0101
Publikováno v:
Automation in Construction, 142
High accuracy 3D surface information is required for many construction robotics tasks such as automated cement polishing or robotic plaster spraying. However, consumer-grade depth cameras currently found in the market are not accurate enough for thes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0f68f6eed832925fd0c637f8f23216b
https://doi.org/10.1016/j.autcon.2022.104424
https://doi.org/10.1016/j.autcon.2022.104424
Publikováno v:
CVPR
The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. Recent approaches have focused on either leveraging
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e44b3182e6f2c876ceb77077abdfbd9
https://doi.org/10.1109/cvpr46437.2021.01664
https://doi.org/10.1109/cvpr46437.2021.01664
Autor:
Martin R. Oswald, Abel Gawel, Niclas Vödisch, Adrian Brandemuehl, Victor Reijgwart, Jen Jen Chung, Benson Kuan, Lukas Schaupp, Mathias Bürki, Hermann Blum, Leiv Andresen, Lukas Bernreiter, Roland Siegwart, Alex Hönger
Publikováno v:
IROS
This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b453a7a08e3171ffd2ae4dc7890be855
https://doi.org/10.1109/iros45743.2020.9341702
https://doi.org/10.1109/iros45743.2020.9341702