Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Simon T. Isele"'
Publikováno v:
ITSC
Localization for Autonomous Driving is mainly researched on camera and LiDAR data, rarely on radar data. Applying a point cloud pose graph SLAM on a modern 77GHz automotive radar sensor-set, we realize direct radar sub-map scan matching for consisten
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7538a0868a7a68fde5011e2a7e88fd33
https://doi.org/10.1109/itsc48978.2021.9564693
https://doi.org/10.1109/itsc48978.2021.9564693
Publikováno v:
2021 IEEE Intelligent Vehicles Symposium (IV).
Localization and perception research for Autonomous Driving is mainly focused on camera and LiDAR data, rarely on radar data. We apply an automated labeling pipeline to semantically annotate real world radar measurements, manually correct point-wise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b4593e7d041d25904e60ee1996fbb26
https://doi.org/10.1109/iv48863.2021.9575779
https://doi.org/10.1109/iv48863.2021.9575779
Autor:
Florian Keck, Marius Zöllner, Daniel Slieter, Jakob Ketterer, Mathis Brosowsky, Simon T. Isele
Publikováno v:
IV
Deep reinforcement learning has been increasingly discussed for solving continuous control tasks in the field of autonomous driving and driver assistance systems. However, trial-and-error learning and the black-box character of neural networks make i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fa66612b423683211a258731d37f26c
https://doi.org/10.1109/iv48863.2021.9575258
https://doi.org/10.1109/iv48863.2021.9575258
Publikováno v:
VEHITS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::262bbf21e547b9ab26570916e5fe4713
https://doi.org/10.5220/0010404700560064
https://doi.org/10.5220/0010404700560064
Publikováno v:
VEHITS
Research on localization and perception for Autonomous Driving is mainly focused on camera and LiDAR datasets, rarely on radar data. Manually labeling sparse radar point clouds is challenging. For a dataset generation, we propose the cross sensor Rad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ab3e6e99d93ab6a20ef923f781d58aa
https://doi.org/10.5220/0010395100220033
https://doi.org/10.5220/0010395100220033