Zobrazeno 1 - 10
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pro vyhledávání: '"Göhring, Daniel"'
LiDAR Semantic Segmentation is a fundamental task in autonomous driving perception consisting of associating each LiDAR point to a semantic label. Fully-supervised models have widely tackled this task, but they require labels for each scan, which eit
Externí odkaz:
http://arxiv.org/abs/2411.02969
Autor:
Quentin, Philipp, Goehring, Daniel
For the use of 6D pose estimation in robotic applications, reliable poses are of utmost importance to ensure a safe, reliable and predictable operational performance. Despite these requirements, state-of-the-art 6D pose estimators often do not provid
Externí odkaz:
http://arxiv.org/abs/2409.03556
Robustness against Out-of-Distribution (OoD) samples is a key performance indicator of a trajectory prediction model. However, the development and ranking of state-of-the-art (SotA) models are driven by their In-Distribution (ID) performance on indiv
Externí odkaz:
http://arxiv.org/abs/2407.13431
Publikováno v:
IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 420-426, Jan. 2024
The precise point cloud ground segmentation is a crucial prerequisite of virtually all perception tasks for LiDAR sensors in autonomous vehicles. Especially the clustering and extraction of objects from a point cloud usually relies on an accurate rem
Externí odkaz:
http://arxiv.org/abs/2405.15664
In complex traffic environments, autonomous vehicles face multi-modal uncertainty about other agents' future behavior. To address this, recent advancements in learningbased motion predictors output multi-modal predictions. We present our novel framew
Externí odkaz:
http://arxiv.org/abs/2405.03470
Model Predictive Control lacks the ability to escape local minima in nonconvex problems. Furthermore, in fast-changing, uncertain environments, the conventional warmstart, using the optimal trajectory from the last timestep, often falls short of prov
Externí odkaz:
http://arxiv.org/abs/2310.02918
Despite the advances in robotics a large proportion of the of parts handling tasks in the automotive industry's internal logistics are not automated but still performed by humans. A key component to competitively automate these processes is a 6D pose
Externí odkaz:
http://arxiv.org/abs/2309.14265
Linear trajectory models provide mathematical advantages to autonomous driving applications such as motion prediction. However, linear models' expressive power and bias for real-world trajectories have not been thoroughly analyzed. We present an in-d
Externí odkaz:
http://arxiv.org/abs/2211.01696
Akademický článek
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Autor:
Göhring, Daniel
Die mobile Robotik stellt ein sehr junges und komplexes Forschungsfelder unserer Zeit dar. Innerhalb der letzten Jahrzehnte wurde es Robotern möglich, sich innerhalb ihrer Umgebung zu bewegen, zu navigieren und mit ihrer Umwelt zu interagieren. Aufg
Externí odkaz:
http://edoc.hu-berlin.de/18452/16692