Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Abel Gawel"'
Publikováno v:
IEEE Transactions on Robotics. :1-18
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:
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
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
Publikováno v:
Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC).
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
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
Publikováno v:
IEEE Robotics and Automation Letters. 3:1687-1694
Global registration of multi-view robot data is a challenging task. Appearance-based global localization approaches often fail under drastic view-point changes, as representations have limited view-point invariance. This work is based on the idea tha
Publikováno v:
IEEE Robotics and Automation Letters, 4 (4)
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters
In this letter, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for aut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db4b4e734eff1727d17b9725aa88c9f5
https://hdl.handle.net/20.500.11850/356431
https://hdl.handle.net/20.500.11850/356431