Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Lukas Bernreiter"'
Autor:
Florian Tschopp, Michael Riner, Marius Fehr, Lukas Bernreiter, Fadri Furrer, Tonci Novkovic, Andreas Pfrunder, Cesar Cadena, Roland Siegwart, Juan Nieto
Publikováno v:
Sensors, Vol 20, Iss 5, p 1439 (2020)
Robust and accurate pose estimation is crucial for many applications in mobile robotics. Extending visual Simultaneous Localization and Mapping (SLAM) with other modalities such as an inertial measurement unit (IMU) can boost robustness and accuracy.
Externí odkaz:
https://doaj.org/article/65195a790f0648f4bc9e8b69f7cdcf8f
Autor:
Andrei Cramariuc, Lukas Bernreiter, Florian Tschopp, Marius Fehr, Victor Reijgwart, Juan Nieto, Roland Siegwart, Cesar Cadena
Publikováno v:
IEEE Robotics and Automation Letters, 8 (2)
Integration of multiple sensor modalities and deep learning into Simultaneous Localization And Mapping (SLAM) systems are areas of significant interest in current research. Multi-modality is a stepping stone towards achieving robustness in challengin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58aec15ee2f5d2ca61c4f982ec14158c
https://hdl.handle.net/20.500.11850/593943
https://hdl.handle.net/20.500.11850/593943
Publikováno v:
IEEE Robotics and Automation Letters. 6:8710-8717
We propose a probabilistic framework for multi-modal global localisation using 3D point correspondences without needing to integrate over SE(3) for Bayesian inference. A finite set of transformation candidates is constructed by decomposing the known
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ab81282d72472d74dbdce86f81c21b9
https://doi.org/10.1109/lra.2021.3114956
https://doi.org/10.1109/lra.2021.3114956
Autor:
Marco Tranzatto, Mihir Kulkarni, Victor Reijgwart, Benoit Casseau, Timon Homberger, Paolo De Petris, Lionel Ott, Wayne Tubby, Gabriel Waibel, Huan Nguyen, Cesar Cadena, Mihir Dharmadhikari, Russell Buchanan, Lorenz Wellhausen, Nikhil Khedekar, Olov Andersson, Lintong Zhang, Takahiro Miki, Tung Dang, Matias Mattamala, Markus Montenegro, Konrad Meyer, Lukas Bernreiter, Xiangyu Wu, Adrien Briod, Mark Mueller, Maurice Fallon, Roland Siegwart, Marco Hutter, Kostas Alexis, Marco Camurri, Shehryar Khattak, Frank Mascarich, Patrick Pfreundschuh, David Wisth, Samuel Zimmermann
Publikováno v:
Web of Science
This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::66c702eb37fa951ae6621a93f0b98aba
http://arxiv.org/abs/2207.04914
http://arxiv.org/abs/2207.04914
Autor:
Marco Tranzatto, Frank Mascarich, Lukas Bernreiter, Carolina Godinho, Marco Camurri, Shehryar Khattak, Tung Dang, Victor Reijgwart, Johannes Löje, David Wisth, Samuel Zimmermann, Huan Nguyen, Marius Fehr, Lukas Solanka, Russell Buchanan, Marko Bjelonic, Nikhil Khedekar, Mathieu Valceschini, Fabian Jenelten, Mihir Dharmadhikari, Timon Homberger, Paolo De Petris, Lorenz Wellhausen, Mihir Kulkarni, Takahiro Miki, Satchel Hirsch, Markus Montenegro, Christos Papachristos, Fabian Tresoldi, Jan Carius, Giorgio Valsecchi, Joonho Lee, Konrad Meyer, Xiangyu Wu, Juan Nieto, Andy Smith, Marco Hutter, Roland Siegwart, Mark Mueller, Maurice Fallon, Kostas Alexis
Publikováno v:
Field Robotics, 2
Field Robotics
Field Robotics
Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems, as underground settings present key challenges that can render robot autonomy hard to achieve. This problem has motivated the DARPA Subterranean Cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53fa9040a9f45dfd3e0bd89a8d017b0c
https://hdl.handle.net/20.500.11850/489726
https://hdl.handle.net/20.500.11850/489726
Publikováno v:
ICRA
In this paper, we propose a robust end-to-end multi-modal pipeline for place recognition where the sensor systems can differ from the map building to the query. Our approach operates directly on images and LiDAR scans without requiring any local feat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8aadea6da65329455c0eccf0078f38c0
Publikováno v:
ICRA
With the advent of powerful, light-weight 3D LiDARs, they have become the hearth of many navigation and SLAM algorithms on various autonomous systems. Pointcloud registration methods working with unstructured pointclouds such as ICP are often computa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0519c8f3c8468b6f2a911433d951479
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
Autor:
Michael Riner, Juan Nieto, Marius Fehr, Tonci Novkovic, Cesar Cadena, Roland Siegwart, Andreas Pfrunder, Lukas Bernreiter, Florian Tschopp, Fadri Furrer
Publikováno v:
Sensors (Basel, Switzerland)
Sensors
Volume 20
Issue 5
Sensors, Vol 20, Iss 5, p 1439 (2020)
Sensors, 20 (5)
Sensors
Volume 20
Issue 5
Sensors, Vol 20, Iss 5, p 1439 (2020)
Sensors, 20 (5)
Robust and accurate pose estimation is crucial for many applications in mobile robotics. Extending visual Simultaneous Localization and Mapping (SLAM) with other modalities such as an inertial measurement unit (IMU) can boost robustness and accuracy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa224d7295d1b5fcf6e9dcbf93399a3d
https://pubmed.ncbi.nlm.nih.gov/32155749
https://pubmed.ncbi.nlm.nih.gov/32155749
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